MODELO FRECUENCIA DIARIA
Edwin Villarraga-Jorge Montoya-Juan David Arango
10/8/2019
EVALUACIÓN DE MODELOS
1. Importación y estandarización de la información
load("../data/Frecuencia_De_Accidentes_Diario.Rda")load("../data/Dias_Especiales_Diario.Rda")Se crean las columnas de accidentes Graves y leves para saber la frecuencia por día
library(reshape)##
## Attaching package: 'reshape'## The following object is masked from 'package:dplyr':
##
## rename## The following objects are masked from 'package:tidyr':
##
## expand, smithsTotal_Dataset_Freq <- cast(Total_Dataset_Freq[,c(1,2,3)],FECHA~GRAVEDAD)## Using FREQ as value column. Use the value argument to cast to override this choiceSe agrega la columna TOTAL_ACCIDENTES
Total_Dataset_Freq$TOTAL_ACCIDENTES <- Total_Dataset_Freq$ACCIDENTES_GRAVES + Total_Dataset_Freq$ACCIDENTES_LEVESTotal_Dataset_Freq <- sqldf("SELECT *
FROM Total_Dataset_Freq
LEFT JOIN Dias_Especiales USING(FECHA)")Total_Dataset_Freq$DIA <-as.factor(format(Total_Dataset_Freq$FECHA,'%u'))save(Total_Dataset_Freq,file="../Modelos/Total_Dataset_Freq_diaria.Rda")2. Partición de los datos para entrenamiento y test
Se ajustarán modelos con la información disponible desde el 01 de enero de 2014 hasta el 31 de diciembre de 2017 y se utilizará el año 2018 para validar el modelo:
Train_D_Dataset <- subset(Total_Dataset_Freq, ANO!="2018")summary(Train_D_Dataset$ANO)## 2014 2015 2016 2017 2018 2019 2020 2021
## 365 365 366 365 0 0 0 0Se ajustan otra vez los niveles del factor ANO
Train_D_Dataset$ANO <- factor(Train_D_Dataset$ANO)summary(Train_D_Dataset$ANO)## 2014 2015 2016 2017
## 365 365 366 365library(sqldf)
Test_D_Dataset <- sqldf("SELECT *
FROM Total_Dataset_Freq
WHERE ANO == 2018")summary(Test_D_Dataset$ANO)## 2014 2015 2016 2017 2018 2019 2020 2021
## 0 0 0 0 365 0 0 0Se ajustan otra vez los niveles del factor ANO
Test_D_Dataset$ANO <- factor(Test_D_Dataset$ANO)summary(Test_D_Dataset$ANO)## 2018
## 3653. Selección de las mejores variables para el modelo
Se utilizará el método forward selection para elegir las mejores variables explicativas del modelo teniendo como criterio aquellas variables que presente mejor R^2 ajustado
Selección de variables para Total accidentes
library (leaps)
regfit.fwd=regsubsets (TOTAL_ACCIDENTES∼ANO+MES+DIA+SEMANA+Feriado_Lunes+Feriado_Otro+Previo_feriado+Prima+Mujer+Padre+Madre+AmoryAmistad+Semana_Santa+Viernes_Antes_Puente+Quincena+Viernes_Desp_Quincena_v1+Viernes_Desp_Quincena_v2+Feria_Flores,Train_D_Dataset, method ="forward", nvmax= 80)## Warning in leaps.setup(x, y, wt = wt, nbest = nbest, nvmax = nvmax,
## force.in = force.in, : 1 linear dependencies found## Reordering variables and trying again:summary (regfit.fwd)## Subset selection object
## Call: regsubsets.formula(TOTAL_ACCIDENTES ~ ANO + MES + DIA + SEMANA +
## Feriado_Lunes + Feriado_Otro + Previo_feriado + Prima + Mujer +
## Padre + Madre + AmoryAmistad + Semana_Santa + Viernes_Antes_Puente +
## Quincena + Viernes_Desp_Quincena_v1 + Viernes_Desp_Quincena_v2 +
## Feria_Flores, Train_D_Dataset, method = "forward", nvmax = 80)
## 86 Variables (and intercept)
## Forced in Forced out
## ANO2015 FALSE FALSE
## ANO2016 FALSE FALSE
## ANO2017 FALSE FALSE
## MES02 FALSE FALSE
## MES03 FALSE FALSE
## MES04 FALSE FALSE
## MES05 FALSE FALSE
## MES06 FALSE FALSE
## MES07 FALSE FALSE
## MES08 FALSE FALSE
## MES09 FALSE FALSE
## MES10 FALSE FALSE
## MES11 FALSE FALSE
## MES12 FALSE FALSE
## DIA2 FALSE FALSE
## DIA3 FALSE FALSE
## DIA4 FALSE FALSE
## DIA5 FALSE FALSE
## DIA6 FALSE FALSE
## DIA7 FALSE FALSE
## SEMANA02 FALSE FALSE
## SEMANA03 FALSE FALSE
## SEMANA04 FALSE FALSE
## SEMANA05 FALSE FALSE
## SEMANA06 FALSE FALSE
## SEMANA07 FALSE FALSE
## SEMANA08 FALSE FALSE
## SEMANA09 FALSE FALSE
## SEMANA10 FALSE FALSE
## SEMANA11 FALSE FALSE
## SEMANA12 FALSE FALSE
## SEMANA13 FALSE FALSE
## SEMANA14 FALSE FALSE
## SEMANA15 FALSE FALSE
## SEMANA16 FALSE FALSE
## SEMANA17 FALSE FALSE
## SEMANA18 FALSE FALSE
## SEMANA19 FALSE FALSE
## SEMANA20 FALSE FALSE
## SEMANA21 FALSE FALSE
## SEMANA22 FALSE FALSE
## SEMANA23 FALSE FALSE
## SEMANA24 FALSE FALSE
## SEMANA25 FALSE FALSE
## SEMANA26 FALSE FALSE
## SEMANA27 FALSE FALSE
## SEMANA28 FALSE FALSE
## SEMANA29 FALSE FALSE
## SEMANA30 FALSE FALSE
## SEMANA31 FALSE FALSE
## SEMANA32 FALSE FALSE
## SEMANA33 FALSE FALSE
## SEMANA34 FALSE FALSE
## SEMANA35 FALSE FALSE
## SEMANA36 FALSE FALSE
## SEMANA37 FALSE FALSE
## SEMANA38 FALSE FALSE
## SEMANA39 FALSE FALSE
## SEMANA40 FALSE FALSE
## SEMANA41 FALSE FALSE
## SEMANA42 FALSE FALSE
## SEMANA43 FALSE FALSE
## SEMANA44 FALSE FALSE
## SEMANA45 FALSE FALSE
## SEMANA46 FALSE FALSE
## SEMANA47 FALSE FALSE
## SEMANA48 FALSE FALSE
## SEMANA49 FALSE FALSE
## SEMANA50 FALSE FALSE
## SEMANA51 FALSE FALSE
## SEMANA52 FALSE FALSE
## SEMANA53 FALSE FALSE
## Feriado_Lunes FALSE FALSE
## Feriado_Otro FALSE FALSE
## Previo_feriado FALSE FALSE
## Mujer FALSE FALSE
## Padre FALSE FALSE
## Madre FALSE FALSE
## AmoryAmistad FALSE FALSE
## Semana_Santa FALSE FALSE
## Viernes_Antes_Puente FALSE FALSE
## Quincena FALSE FALSE
## Viernes_Desp_Quincena_v1 FALSE FALSE
## Viernes_Desp_Quincena_v2 FALSE FALSE
## Feria_Flores FALSE FALSE
## Prima FALSE FALSE
## 1 subsets of each size up to 81
## Selection Algorithm: forward
## ANO2015 ANO2016 ANO2017 MES02 MES03 MES04 MES05 MES06 MES07
## 1 ( 1 ) " " " " " " " " " " " " " " " " " "
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## 19 ( 1 ) " " " " " " " " " " " " " " " " " "
## 20 ( 1 ) " " " " " " " " " " " " " " " " " "
## 21 ( 1 ) " " " " " " " " "*" " " " " " " " "
## 22 ( 1 ) " " " " " " " " "*" " " " " " " " "
## 23 ( 1 ) " " " " " " " " "*" " " " " " " " "
## 24 ( 1 ) " " " " " " " " "*" " " "*" " " " "
## 25 ( 1 ) " " " " " " " " "*" " " "*" " " " "
## 26 ( 1 ) " " " " " " " " "*" " " "*" " " " "
## 27 ( 1 ) " " " " " " " " "*" " " "*" " " " "
## 28 ( 1 ) " " " " " " "*" "*" " " "*" " " " "
## 29 ( 1 ) " " " " " " "*" "*" " " "*" " " "*"
## 30 ( 1 ) " " " " " " "*" "*" " " "*" " " "*"
## 31 ( 1 ) " " " " " " "*" "*" " " "*" " " "*"
## 32 ( 1 ) " " " " " " "*" "*" " " "*" " " "*"
## 33 ( 1 ) " " " " " " "*" "*" " " "*" " " "*"
## 34 ( 1 ) " " " " " " "*" "*" "*" "*" " " "*"
## 35 ( 1 ) " " " " " " "*" "*" "*" "*" " " "*"
## 36 ( 1 ) " " " " " " "*" "*" "*" "*" " " "*"
## 37 ( 1 ) " " " " " " "*" "*" "*" "*" " " "*"
## 38 ( 1 ) " " " " " " "*" "*" "*" "*" " " "*"
## 39 ( 1 ) " " " " " " "*" "*" "*" "*" " " "*"
## 40 ( 1 ) " " " " " " "*" "*" "*" "*" "*" "*"
## 41 ( 1 ) " " "*" " " "*" "*" "*" "*" "*" "*"
## 42 ( 1 ) " " "*" "*" "*" "*" "*" "*" "*" "*"
## 43 ( 1 ) " " "*" "*" "*" "*" "*" "*" "*" "*"
## 44 ( 1 ) " " "*" "*" "*" "*" "*" "*" "*" "*"
## 45 ( 1 ) "*" "*" "*" "*" "*" "*" "*" "*" "*"
## 46 ( 1 ) "*" "*" "*" "*" "*" "*" "*" "*" "*"
## 47 ( 1 ) "*" "*" "*" "*" "*" "*" "*" "*" "*"
## 48 ( 1 ) "*" "*" "*" "*" "*" "*" "*" "*" "*"
## 49 ( 1 ) "*" "*" "*" "*" "*" "*" "*" "*" "*"
## 50 ( 1 ) "*" "*" "*" "*" "*" "*" "*" "*" "*"
## 51 ( 1 ) "*" "*" "*" "*" "*" "*" "*" "*" "*"
## 52 ( 1 ) "*" "*" "*" "*" "*" "*" "*" "*" "*"
## 53 ( 1 ) "*" "*" "*" "*" "*" "*" "*" "*" "*"
## 54 ( 1 ) "*" "*" "*" "*" "*" "*" "*" "*" "*"
## 55 ( 1 ) "*" "*" "*" "*" "*" "*" "*" "*" "*"
## 56 ( 1 ) "*" "*" "*" "*" "*" "*" "*" "*" "*"
## 57 ( 1 ) "*" "*" "*" "*" "*" "*" "*" "*" "*"
## 58 ( 1 ) "*" "*" "*" "*" "*" "*" "*" "*" "*"
## 59 ( 1 ) "*" "*" "*" "*" "*" "*" "*" "*" "*"
## 60 ( 1 ) "*" "*" "*" "*" "*" "*" "*" "*" "*"
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## 65 ( 1 ) "*" "*" "*" "*" "*" "*" "*" "*" "*"
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## 72 ( 1 ) "*" "*" "*" "*" "*" "*" "*" "*" "*"
## 73 ( 1 ) "*" "*" "*" "*" "*" "*" "*" "*" "*"
## 74 ( 1 ) "*" "*" "*" "*" "*" "*" "*" "*" "*"
## 75 ( 1 ) "*" "*" "*" "*" "*" "*" "*" "*" "*"
## 76 ( 1 ) "*" "*" "*" "*" "*" "*" "*" "*" "*"
## 77 ( 1 ) "*" "*" "*" "*" "*" "*" "*" "*" "*"
## 78 ( 1 ) "*" "*" "*" "*" "*" "*" "*" "*" "*"
## 79 ( 1 ) "*" "*" "*" "*" "*" "*" "*" "*" "*"
## 80 ( 1 ) "*" "*" "*" "*" "*" "*" "*" "*" "*"
## 81 ( 1 ) "*" "*" "*" "*" "*" "*" "*" "*" "*"
## MES08 MES09 MES10 MES11 MES12 DIA2 DIA3 DIA4 DIA5 DIA6 DIA7
## 1 ( 1 ) " " " " " " " " " " " " " " " " " " " " "*"
## 2 ( 1 ) " " " " " " " " " " " " " " " " " " " " "*"
## 3 ( 1 ) " " " " " " " " " " " " " " " " " " " " "*"
## 4 ( 1 ) " " " " " " " " " " " " " " " " " " "*" "*"
## 5 ( 1 ) " " " " " " " " " " " " " " " " " " "*" "*"
## 6 ( 1 ) " " " " " " " " " " " " " " " " " " "*" "*"
## 7 ( 1 ) " " " " " " " " " " " " " " " " " " "*" "*"
## 8 ( 1 ) " " " " " " " " " " " " " " " " " " "*" "*"
## 9 ( 1 ) " " " " " " " " " " " " " " " " " " "*" "*"
## 10 ( 1 ) " " " " " " " " " " " " " " " " " " "*" "*"
## 11 ( 1 ) " " " " " " " " " " " " " " " " " " "*" "*"
## 12 ( 1 ) " " " " " " " " " " " " " " " " " " "*" "*"
## 13 ( 1 ) " " " " " " " " " " " " " " " " " " "*" "*"
## 14 ( 1 ) " " " " " " " " " " " " " " " " " " "*" "*"
## 15 ( 1 ) " " " " " " " " " " " " " " " " "*" "*" "*"
## 16 ( 1 ) " " " " " " " " " " " " " " " " "*" "*" "*"
## 17 ( 1 ) " " " " " " " " " " " " " " " " "*" "*" "*"
## 18 ( 1 ) " " " " " " " " " " " " " " " " "*" "*" "*"
## 19 ( 1 ) " " " " " " " " " " " " " " " " "*" "*" "*"
## 20 ( 1 ) " " " " " " " " " " " " " " " " "*" "*" "*"
## 21 ( 1 ) " " " " " " " " " " " " " " " " "*" "*" "*"
## 22 ( 1 ) " " " " " " " " " " " " " " " " "*" "*" "*"
## 23 ( 1 ) "*" " " " " " " " " " " " " " " "*" "*" "*"
## 24 ( 1 ) "*" " " " " " " " " " " " " " " "*" "*" "*"
## 25 ( 1 ) "*" " " " " " " " " " " " " " " "*" "*" "*"
## 26 ( 1 ) "*" " " " " " " " " " " " " " " "*" "*" "*"
## 27 ( 1 ) "*" " " " " " " " " " " " " " " "*" "*" "*"
## 28 ( 1 ) "*" " " " " " " " " " " " " " " "*" "*" "*"
## 29 ( 1 ) "*" " " " " " " " " " " " " " " "*" "*" "*"
## 30 ( 1 ) "*" " " "*" " " " " " " " " " " "*" "*" "*"
## 31 ( 1 ) "*" "*" "*" " " " " " " " " " " "*" "*" "*"
## 32 ( 1 ) "*" "*" "*" "*" " " " " " " " " "*" "*" "*"
## 33 ( 1 ) "*" "*" "*" "*" " " " " " " " " "*" "*" "*"
## 34 ( 1 ) "*" "*" "*" "*" " " " " " " " " "*" "*" "*"
## 35 ( 1 ) "*" "*" "*" "*" " " " " " " " " "*" "*" "*"
## 36 ( 1 ) "*" "*" "*" "*" " " " " "*" " " "*" "*" "*"
## 37 ( 1 ) "*" "*" "*" "*" " " " " "*" "*" "*" "*" "*"
## 38 ( 1 ) "*" "*" "*" "*" " " " " "*" "*" "*" "*" "*"
## 39 ( 1 ) "*" "*" "*" "*" " " " " "*" "*" "*" "*" "*"
## 40 ( 1 ) "*" "*" "*" "*" " " " " "*" "*" "*" "*" "*"
## 41 ( 1 ) "*" "*" "*" "*" " " " " "*" "*" "*" "*" "*"
## 42 ( 1 ) "*" "*" "*" "*" " " " " "*" "*" "*" "*" "*"
## 43 ( 1 ) "*" "*" "*" "*" " " " " "*" "*" "*" "*" "*"
## 44 ( 1 ) "*" "*" "*" "*" " " " " "*" "*" "*" "*" "*"
## 45 ( 1 ) "*" "*" "*" "*" " " " " "*" "*" "*" "*" "*"
## 46 ( 1 ) "*" "*" "*" "*" " " " " "*" "*" "*" "*" "*"
## 47 ( 1 ) "*" "*" "*" "*" " " " " "*" "*" "*" "*" "*"
## 48 ( 1 ) "*" "*" "*" "*" " " " " "*" "*" "*" "*" "*"
## 49 ( 1 ) "*" "*" "*" "*" " " " " "*" "*" "*" "*" "*"
## 50 ( 1 ) "*" "*" "*" "*" " " " " "*" "*" "*" "*" "*"
## 51 ( 1 ) "*" "*" "*" "*" " " " " "*" "*" "*" "*" "*"
## 52 ( 1 ) "*" "*" "*" "*" " " " " "*" "*" "*" "*" "*"
## 53 ( 1 ) "*" "*" "*" "*" " " " " "*" "*" "*" "*" "*"
## 54 ( 1 ) "*" "*" "*" "*" " " " " "*" "*" "*" "*" "*"
## 55 ( 1 ) "*" "*" "*" "*" " " " " "*" "*" "*" "*" "*"
## 56 ( 1 ) "*" "*" "*" "*" " " " " "*" "*" "*" "*" "*"
## 57 ( 1 ) "*" "*" "*" "*" " " " " "*" "*" "*" "*" "*"
## 58 ( 1 ) "*" "*" "*" "*" " " " " "*" "*" "*" "*" "*"
## 59 ( 1 ) "*" "*" "*" "*" " " " " "*" "*" "*" "*" "*"
## 60 ( 1 ) "*" "*" "*" "*" " " " " "*" "*" "*" "*" "*"
## 61 ( 1 ) "*" "*" "*" "*" " " " " "*" "*" "*" "*" "*"
## 62 ( 1 ) "*" "*" "*" "*" " " " " "*" "*" "*" "*" "*"
## 63 ( 1 ) "*" "*" "*" "*" " " " " "*" "*" "*" "*" "*"
## 64 ( 1 ) "*" "*" "*" "*" " " " " "*" "*" "*" "*" "*"
## 65 ( 1 ) "*" "*" "*" "*" " " " " "*" "*" "*" "*" "*"
## 66 ( 1 ) "*" "*" "*" "*" " " " " "*" "*" "*" "*" "*"
## 67 ( 1 ) "*" "*" "*" "*" " " " " "*" "*" "*" "*" "*"
## 68 ( 1 ) "*" "*" "*" "*" " " " " "*" "*" "*" "*" "*"
## 69 ( 1 ) "*" "*" "*" "*" " " " " "*" "*" "*" "*" "*"
## 70 ( 1 ) "*" "*" "*" "*" " " " " "*" "*" "*" "*" "*"
## 71 ( 1 ) "*" "*" "*" "*" " " " " "*" "*" "*" "*" "*"
## 72 ( 1 ) "*" "*" "*" "*" " " " " "*" "*" "*" "*" "*"
## 73 ( 1 ) "*" "*" "*" "*" " " " " "*" "*" "*" "*" "*"
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## 75 ( 1 ) "*" "*" "*" "*" " " " " "*" "*" "*" "*" "*"
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## 80 ( 1 ) "*" "*" "*" "*" " " " " "*" "*" "*" "*" "*"
## 81 ( 1 ) "*" "*" "*" "*" " " " " "*" "*" "*" "*" "*"
## SEMANA02 SEMANA03 SEMANA04 SEMANA05 SEMANA06 SEMANA07 SEMANA08
## 1 ( 1 ) " " " " " " " " " " " " " "
## 2 ( 1 ) " " " " " " " " " " " " " "
## 3 ( 1 ) " " " " " " " " " " " " " "
## 4 ( 1 ) " " " " " " " " " " " " " "
## 5 ( 1 ) "*" " " " " " " " " " " " "
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## 7 ( 1 ) "*" " " " " " " " " " " " "
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## 10 ( 1 ) "*" " " " " " " " " " " " "
## 11 ( 1 ) "*" " " " " " " " " " " " "
## 12 ( 1 ) "*" "*" " " " " " " " " " "
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## 27 ( 1 ) "*" "*" "*" " " " " " " " "
## 28 ( 1 ) "*" "*" "*" " " " " " " " "
## 29 ( 1 ) "*" "*" "*" " " " " " " " "
## 30 ( 1 ) "*" "*" "*" " " " " " " " "
## 31 ( 1 ) "*" "*" "*" " " " " " " " "
## 32 ( 1 ) "*" "*" "*" " " " " " " " "
## 33 ( 1 ) "*" "*" "*" " " " " " " " "
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## 35 ( 1 ) "*" "*" "*" "*" " " " " " "
## 36 ( 1 ) "*" "*" "*" "*" " " " " " "
## 37 ( 1 ) "*" "*" "*" "*" " " " " " "
## 38 ( 1 ) "*" "*" "*" "*" " " " " " "
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## 40 ( 1 ) "*" "*" "*" "*" " " " " " "
## 41 ( 1 ) "*" "*" "*" "*" " " " " " "
## 42 ( 1 ) "*" "*" "*" "*" " " " " " "
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## 48 ( 1 ) "*" "*" "*" "*" " " " " " "
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## 50 ( 1 ) "*" "*" "*" "*" " " "*" " "
## 51 ( 1 ) "*" "*" "*" "*" " " "*" " "
## 52 ( 1 ) "*" "*" "*" "*" " " "*" " "
## 53 ( 1 ) "*" "*" "*" "*" " " "*" " "
## 54 ( 1 ) "*" "*" "*" "*" " " "*" " "
## 55 ( 1 ) "*" "*" "*" "*" " " "*" " "
## 56 ( 1 ) "*" "*" "*" "*" " " "*" " "
## 57 ( 1 ) "*" "*" "*" "*" " " "*" " "
## 58 ( 1 ) "*" "*" "*" "*" " " "*" " "
## 59 ( 1 ) "*" "*" "*" "*" " " "*" " "
## 60 ( 1 ) "*" "*" "*" "*" " " "*" " "
## 61 ( 1 ) "*" "*" "*" "*" " " "*" " "
## 62 ( 1 ) "*" "*" "*" "*" " " "*" " "
## 63 ( 1 ) "*" "*" "*" "*" "*" "*" " "
## 64 ( 1 ) "*" "*" "*" "*" "*" "*" "*"
## 65 ( 1 ) "*" "*" "*" "*" "*" "*" "*"
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## 80 ( 1 ) "*" "*" "*" "*" "*" "*" "*"
## 81 ( 1 ) "*" "*" "*" "*" "*" "*" "*"
## SEMANA09 SEMANA10 SEMANA11 SEMANA12 SEMANA13 SEMANA14 SEMANA15
## 1 ( 1 ) " " " " " " " " " " " " " "
## 2 ( 1 ) " " " " " " " " " " " " " "
## 3 ( 1 ) " " " " " " " " " " " " " "
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## 23 ( 1 ) " " " " " " " " " " " " " "
## 24 ( 1 ) " " " " " " " " " " " " " "
## 25 ( 1 ) " " " " " " " " " " "*" " "
## 26 ( 1 ) " " " " " " " " " " "*" "*"
## 27 ( 1 ) " " " " " " " " " " "*" "*"
## 28 ( 1 ) " " " " " " " " " " "*" "*"
## 29 ( 1 ) " " " " " " " " " " "*" "*"
## 30 ( 1 ) " " " " " " " " " " "*" "*"
## 31 ( 1 ) " " " " " " " " " " "*" "*"
## 32 ( 1 ) " " " " " " " " " " "*" "*"
## 33 ( 1 ) " " " " " " " " " " "*" "*"
## 34 ( 1 ) " " " " " " " " " " "*" "*"
## 35 ( 1 ) " " " " " " " " " " "*" "*"
## 36 ( 1 ) " " " " " " " " " " "*" "*"
## 37 ( 1 ) " " " " " " " " " " "*" "*"
## 38 ( 1 ) " " " " " " " " " " "*" "*"
## 39 ( 1 ) " " " " " " " " " " "*" "*"
## 40 ( 1 ) " " " " " " " " " " "*" "*"
## 41 ( 1 ) " " " " " " " " " " "*" "*"
## 42 ( 1 ) " " " " " " " " " " "*" "*"
## 43 ( 1 ) " " " " " " " " " " "*" "*"
## 44 ( 1 ) " " " " " " " " " " "*" "*"
## 45 ( 1 ) " " " " " " " " " " "*" "*"
## 46 ( 1 ) " " " " " " " " " " "*" "*"
## 47 ( 1 ) " " " " " " "*" " " "*" "*"
## 48 ( 1 ) " " " " " " "*" " " "*" "*"
## 49 ( 1 ) " " " " " " "*" " " "*" "*"
## 50 ( 1 ) " " " " " " "*" " " "*" "*"
## 51 ( 1 ) " " " " " " "*" " " "*" "*"
## 52 ( 1 ) " " " " " " "*" " " "*" "*"
## 53 ( 1 ) " " " " " " "*" " " "*" "*"
## 54 ( 1 ) " " " " " " "*" " " "*" "*"
## 55 ( 1 ) " " " " " " "*" " " "*" "*"
## 56 ( 1 ) " " " " " " "*" " " "*" "*"
## 57 ( 1 ) "*" " " " " "*" " " "*" "*"
## 58 ( 1 ) "*" " " " " "*" " " "*" "*"
## 59 ( 1 ) "*" " " " " "*" " " "*" "*"
## 60 ( 1 ) "*" " " " " "*" " " "*" "*"
## 61 ( 1 ) "*" " " " " "*" " " "*" "*"
## 62 ( 1 ) "*" " " " " "*" " " "*" "*"
## 63 ( 1 ) "*" " " " " "*" " " "*" "*"
## 64 ( 1 ) "*" " " " " "*" " " "*" "*"
## 65 ( 1 ) "*" "*" " " "*" " " "*" "*"
## 66 ( 1 ) "*" "*" "*" "*" " " "*" "*"
## 67 ( 1 ) "*" "*" "*" "*" "*" "*" "*"
## 68 ( 1 ) "*" "*" "*" "*" "*" "*" "*"
## 69 ( 1 ) "*" "*" "*" "*" "*" "*" "*"
## 70 ( 1 ) "*" "*" "*" "*" "*" "*" "*"
## 71 ( 1 ) "*" "*" "*" "*" "*" "*" "*"
## 72 ( 1 ) "*" "*" "*" "*" "*" "*" "*"
## 73 ( 1 ) "*" "*" "*" "*" "*" "*" "*"
## 74 ( 1 ) "*" "*" "*" "*" "*" "*" "*"
## 75 ( 1 ) "*" "*" "*" "*" "*" "*" "*"
## 76 ( 1 ) "*" "*" "*" "*" "*" "*" "*"
## 77 ( 1 ) "*" "*" "*" "*" "*" "*" "*"
## 78 ( 1 ) "*" "*" "*" "*" "*" "*" "*"
## 79 ( 1 ) "*" "*" "*" "*" "*" "*" "*"
## 80 ( 1 ) "*" "*" "*" "*" "*" "*" "*"
## 81 ( 1 ) "*" "*" "*" "*" "*" "*" "*"
## SEMANA16 SEMANA17 SEMANA18 SEMANA19 SEMANA20 SEMANA21 SEMANA22
## 1 ( 1 ) " " " " " " " " " " " " " "
## 2 ( 1 ) " " " " " " " " " " " " " "
## 3 ( 1 ) " " " " " " " " " " " " " "
## 4 ( 1 ) " " " " " " " " " " " " " "
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## 10 ( 1 ) " " " " " " " " " " " " " "
## 11 ( 1 ) " " " " " " " " " " " " " "
## 12 ( 1 ) " " " " " " " " " " " " " "
## 13 ( 1 ) " " " " " " " " " " " " " "
## 14 ( 1 ) " " " " " " " " " " " " " "
## 15 ( 1 ) " " " " " " " " " " " " " "
## 16 ( 1 ) " " " " " " " " " " " " " "
## 17 ( 1 ) " " " " " " " " " " " " " "
## 18 ( 1 ) " " " " " " " " " " " " " "
## 19 ( 1 ) " " " " " " " " " " " " " "
## 20 ( 1 ) " " " " " " " " " " " " " "
## 21 ( 1 ) " " " " " " " " " " " " " "
## 22 ( 1 ) " " "*" " " " " " " " " " "
## 23 ( 1 ) " " "*" " " " " " " " " " "
## 24 ( 1 ) " " "*" " " " " " " " " " "
## 25 ( 1 ) " " "*" " " " " " " " " " "
## 26 ( 1 ) " " "*" " " " " " " " " " "
## 27 ( 1 ) " " "*" " " " " " " " " " "
## 28 ( 1 ) " " "*" " " " " " " " " " "
## 29 ( 1 ) " " "*" " " " " " " " " " "
## 30 ( 1 ) " " "*" " " " " " " " " " "
## 31 ( 1 ) " " "*" " " " " " " " " " "
## 32 ( 1 ) " " "*" " " " " " " " " " "
## 33 ( 1 ) " " "*" " " " " " " " " " "
## 34 ( 1 ) " " "*" " " " " " " " " " "
## 35 ( 1 ) " " "*" " " " " " " " " " "
## 36 ( 1 ) " " "*" " " " " " " " " " "
## 37 ( 1 ) " " "*" " " " " " " " " " "
## 38 ( 1 ) " " "*" " " " " " " " " " "
## 39 ( 1 ) " " "*" " " " " " " " " " "
## 40 ( 1 ) " " "*" " " " " " " " " " "
## 41 ( 1 ) " " "*" " " " " " " " " " "
## 42 ( 1 ) " " "*" " " " " " " " " " "
## 43 ( 1 ) " " "*" " " " " " " " " " "
## 44 ( 1 ) " " "*" " " " " " " " " " "
## 45 ( 1 ) " " "*" " " " " " " " " " "
## 46 ( 1 ) " " "*" "*" " " " " " " " "
## 47 ( 1 ) " " "*" "*" " " " " " " " "
## 48 ( 1 ) " " "*" "*" " " " " " " " "
## 49 ( 1 ) " " "*" "*" " " " " " " " "
## 50 ( 1 ) " " "*" "*" " " " " " " " "
## 51 ( 1 ) " " "*" "*" " " " " " " " "
## 52 ( 1 ) " " "*" "*" " " " " " " "*"
## 53 ( 1 ) " " "*" "*" " " " " " " "*"
## 54 ( 1 ) " " "*" "*" " " " " " " "*"
## 55 ( 1 ) " " "*" "*" " " " " " " "*"
## 56 ( 1 ) " " "*" "*" " " " " " " "*"
## 57 ( 1 ) " " "*" "*" " " " " " " "*"
## 58 ( 1 ) " " "*" "*" " " " " " " "*"
## 59 ( 1 ) " " "*" "*" " " " " " " "*"
## 60 ( 1 ) " " "*" "*" " " " " " " "*"
## 61 ( 1 ) " " "*" "*" " " " " " " "*"
## 62 ( 1 ) " " "*" "*" " " " " " " "*"
## 63 ( 1 ) " " "*" "*" " " " " " " "*"
## 64 ( 1 ) " " "*" "*" " " " " " " "*"
## 65 ( 1 ) " " "*" "*" " " " " " " "*"
## 66 ( 1 ) " " "*" "*" " " " " " " "*"
## 67 ( 1 ) " " "*" "*" " " " " " " "*"
## 68 ( 1 ) "*" "*" "*" " " " " " " "*"
## 69 ( 1 ) "*" "*" "*" " " " " " " "*"
## 70 ( 1 ) "*" "*" "*" " " " " " " "*"
## 71 ( 1 ) "*" "*" "*" " " " " " " "*"
## 72 ( 1 ) "*" "*" "*" " " "*" " " "*"
## 73 ( 1 ) "*" "*" "*" " " "*" " " "*"
## 74 ( 1 ) "*" "*" "*" " " "*" "*" "*"
## 75 ( 1 ) "*" "*" "*" "*" "*" "*" "*"
## 76 ( 1 ) "*" "*" "*" "*" "*" "*" "*"
## 77 ( 1 ) "*" "*" "*" "*" "*" "*" "*"
## 78 ( 1 ) "*" "*" "*" "*" "*" "*" "*"
## 79 ( 1 ) "*" "*" "*" "*" "*" "*" "*"
## 80 ( 1 ) "*" "*" "*" "*" "*" "*" "*"
## 81 ( 1 ) "*" "*" "*" "*" "*" "*" "*"
## SEMANA23 SEMANA24 SEMANA25 SEMANA26 SEMANA27 SEMANA28 SEMANA29
## 1 ( 1 ) " " " " " " " " " " " " " "
## 2 ( 1 ) " " " " " " " " " " " " " "
## 3 ( 1 ) " " " " " " " " " " " " " "
## 4 ( 1 ) " " " " " " " " " " " " " "
## 5 ( 1 ) " " " " " " " " " " " " " "
## 6 ( 1 ) " " " " " " " " " " " " " "
## 7 ( 1 ) " " " " " " " " " " " " " "
## 8 ( 1 ) " " " " " " " " " " " " " "
## 9 ( 1 ) " " " " " " " " " " " " " "
## 10 ( 1 ) " " " " " " "*" " " " " " "
## 11 ( 1 ) " " " " " " "*" " " " " " "
## 12 ( 1 ) " " " " " " "*" " " " " " "
## 13 ( 1 ) " " " " " " "*" " " " " " "
## 14 ( 1 ) " " " " " " "*" " " " " " "
## 15 ( 1 ) " " " " " " "*" " " " " " "
## 16 ( 1 ) " " " " " " "*" " " " " " "
## 17 ( 1 ) " " " " " " "*" " " " " " "
## 18 ( 1 ) " " " " " " "*" " " " " " "
## 19 ( 1 ) " " " " " " "*" " " " " " "
## 20 ( 1 ) " " " " " " "*" " " " " " "
## 21 ( 1 ) " " " " " " "*" " " " " " "
## 22 ( 1 ) " " " " " " "*" " " " " " "
## 23 ( 1 ) " " " " " " "*" " " " " " "
## 24 ( 1 ) " " " " " " "*" " " " " " "
## 25 ( 1 ) " " " " " " "*" " " " " " "
## 26 ( 1 ) " " " " " " "*" " " " " " "
## 27 ( 1 ) "*" " " " " "*" " " " " " "
## 28 ( 1 ) "*" " " " " "*" " " " " " "
## 29 ( 1 ) "*" " " " " "*" " " " " " "
## 30 ( 1 ) "*" " " " " "*" " " " " " "
## 31 ( 1 ) "*" " " " " "*" " " " " " "
## 32 ( 1 ) "*" " " " " "*" " " " " " "
## 33 ( 1 ) "*" " " " " "*" " " " " " "
## 34 ( 1 ) "*" " " " " "*" " " " " " "
## 35 ( 1 ) "*" " " " " "*" " " " " " "
## 36 ( 1 ) "*" " " " " "*" " " " " " "
## 37 ( 1 ) "*" " " " " "*" " " " " " "
## 38 ( 1 ) "*" " " " " "*" " " " " " "
## 39 ( 1 ) "*" " " " " "*" " " " " " "
## 40 ( 1 ) "*" " " " " "*" " " " " " "
## 41 ( 1 ) "*" " " " " "*" " " " " " "
## 42 ( 1 ) "*" " " " " "*" " " " " " "
## 43 ( 1 ) "*" " " " " "*" " " " " " "
## 44 ( 1 ) "*" " " " " "*" " " " " " "
## 45 ( 1 ) "*" " " " " "*" " " " " " "
## 46 ( 1 ) "*" " " " " "*" " " " " " "
## 47 ( 1 ) "*" " " " " "*" " " " " " "
## 48 ( 1 ) "*" " " " " "*" " " " " " "
## 49 ( 1 ) "*" " " " " "*" " " " " " "
## 50 ( 1 ) "*" " " " " "*" " " " " " "
## 51 ( 1 ) "*" " " "*" "*" " " " " " "
## 52 ( 1 ) "*" " " "*" "*" " " " " " "
## 53 ( 1 ) "*" " " "*" "*" " " " " " "
## 54 ( 1 ) "*" " " "*" "*" " " " " " "
## 55 ( 1 ) "*" " " "*" "*" " " " " "*"
## 56 ( 1 ) "*" " " "*" "*" " " " " "*"
## 57 ( 1 ) "*" " " "*" "*" " " " " "*"
## 58 ( 1 ) "*" " " "*" "*" " " " " "*"
## 59 ( 1 ) "*" " " "*" "*" " " " " "*"
## 60 ( 1 ) "*" " " "*" "*" " " " " "*"
## 61 ( 1 ) "*" " " "*" "*" " " " " "*"
## 62 ( 1 ) "*" " " "*" "*" " " " " "*"
## 63 ( 1 ) "*" " " "*" "*" " " " " "*"
## 64 ( 1 ) "*" " " "*" "*" " " " " "*"
## 65 ( 1 ) "*" " " "*" "*" " " " " "*"
## 66 ( 1 ) "*" " " "*" "*" " " " " "*"
## 67 ( 1 ) "*" " " "*" "*" " " " " "*"
## 68 ( 1 ) "*" " " "*" "*" " " " " "*"
## 69 ( 1 ) "*" " " "*" "*" " " " " "*"
## 70 ( 1 ) "*" " " "*" "*" " " " " "*"
## 71 ( 1 ) "*" " " "*" "*" " " "*" "*"
## 72 ( 1 ) "*" " " "*" "*" " " "*" "*"
## 73 ( 1 ) "*" " " "*" "*" " " "*" "*"
## 74 ( 1 ) "*" " " "*" "*" " " "*" "*"
## 75 ( 1 ) "*" " " "*" "*" " " "*" "*"
## 76 ( 1 ) "*" "*" "*" "*" " " "*" "*"
## 77 ( 1 ) "*" "*" "*" "*" "*" "*" "*"
## 78 ( 1 ) "*" "*" "*" "*" "*" "*" "*"
## 79 ( 1 ) "*" "*" "*" "*" "*" "*" "*"
## 80 ( 1 ) "*" "*" "*" "*" "*" "*" "*"
## 81 ( 1 ) "*" "*" "*" "*" "*" "*" "*"
## SEMANA30 SEMANA31 SEMANA32 SEMANA33 SEMANA34 SEMANA35 SEMANA36
## 1 ( 1 ) " " " " " " " " " " " " " "
## 2 ( 1 ) " " " " " " " " " " " " " "
## 3 ( 1 ) " " " " " " " " " " " " " "
## 4 ( 1 ) " " " " " " " " " " " " " "
## 5 ( 1 ) " " " " " " " " " " " " " "
## 6 ( 1 ) " " " " " " " " " " " " " "
## 7 ( 1 ) " " " " " " " " " " " " " "
## 8 ( 1 ) " " " " " " " " " " " " " "
## 9 ( 1 ) " " " " " " " " " " " " " "
## 10 ( 1 ) " " " " " " " " " " " " " "
## 11 ( 1 ) " " " " " " " " " " " " " "
## 12 ( 1 ) " " " " " " " " " " " " " "
## 13 ( 1 ) " " " " " " " " " " " " " "
## 14 ( 1 ) " " " " " " " " " " " " " "
## 15 ( 1 ) " " " " " " " " " " " " " "
## 16 ( 1 ) " " " " " " " " " " " " " "
## 17 ( 1 ) " " " " " " " " " " " " " "
## 18 ( 1 ) " " " " " " " " " " " " " "
## 19 ( 1 ) " " " " " " " " " " " " " "
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## 21 ( 1 ) " " " " " " " " " " " " " "
## 22 ( 1 ) " " " " " " " " " " " " " "
## 23 ( 1 ) " " " " " " " " " " " " " "
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## 26 ( 1 ) " " " " " " " " " " " " " "
## 27 ( 1 ) " " " " " " " " " " " " " "
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## 29 ( 1 ) " " " " " " " " " " " " " "
## 30 ( 1 ) " " " " " " " " " " " " " "
## 31 ( 1 ) " " " " " " " " " " " " " "
## 32 ( 1 ) " " " " " " " " " " " " " "
## 33 ( 1 ) " " " " " " " " " " " " " "
## 34 ( 1 ) " " " " " " " " " " " " " "
## 35 ( 1 ) " " " " " " " " " " " " " "
## 36 ( 1 ) " " " " " " " " " " " " " "
## 37 ( 1 ) " " " " " " " " " " " " " "
## 38 ( 1 ) " " " " " " " " " " " " " "
## 39 ( 1 ) " " " " " " " " " " " " " "
## 40 ( 1 ) " " " " " " " " " " " " " "
## 41 ( 1 ) " " " " " " " " " " " " " "
## 42 ( 1 ) " " " " " " " " " " " " " "
## 43 ( 1 ) " " " " " " " " "*" " " " "
## 44 ( 1 ) " " " " " " " " "*" " " " "
## 45 ( 1 ) " " " " " " " " "*" " " " "
## 46 ( 1 ) " " " " " " " " "*" " " " "
## 47 ( 1 ) " " " " " " " " "*" " " " "
## 48 ( 1 ) " " " " " " " " "*" " " " "
## 49 ( 1 ) " " "*" " " " " "*" " " " "
## 50 ( 1 ) " " "*" " " " " "*" " " " "
## 51 ( 1 ) " " "*" " " " " "*" " " " "
## 52 ( 1 ) " " "*" " " " " "*" " " " "
## 53 ( 1 ) " " "*" " " " " "*" " " " "
## 54 ( 1 ) " " "*" " " " " "*" " " " "
## 55 ( 1 ) " " "*" " " " " "*" " " " "
## 56 ( 1 ) "*" "*" " " " " "*" " " " "
## 57 ( 1 ) "*" "*" " " " " "*" " " " "
## 58 ( 1 ) "*" "*" " " " " "*" " " " "
## 59 ( 1 ) "*" "*" " " " " "*" " " " "
## 60 ( 1 ) "*" "*" " " " " "*" " " " "
## 61 ( 1 ) "*" "*" "*" " " "*" " " " "
## 62 ( 1 ) "*" "*" "*" " " "*" " " " "
## 63 ( 1 ) "*" "*" "*" " " "*" " " " "
## 64 ( 1 ) "*" "*" "*" " " "*" " " " "
## 65 ( 1 ) "*" "*" "*" " " "*" " " " "
## 66 ( 1 ) "*" "*" "*" " " "*" " " " "
## 67 ( 1 ) "*" "*" "*" " " "*" " " " "
## 68 ( 1 ) "*" "*" "*" " " "*" " " " "
## 69 ( 1 ) "*" "*" "*" " " "*" " " " "
## 70 ( 1 ) "*" "*" "*" " " "*" " " " "
## 71 ( 1 ) "*" "*" "*" " " "*" " " " "
## 72 ( 1 ) "*" "*" "*" " " "*" " " " "
## 73 ( 1 ) "*" "*" "*" " " "*" " " "*"
## 74 ( 1 ) "*" "*" "*" " " "*" " " "*"
## 75 ( 1 ) "*" "*" "*" " " "*" " " "*"
## 76 ( 1 ) "*" "*" "*" " " "*" " " "*"
## 77 ( 1 ) "*" "*" "*" " " "*" " " "*"
## 78 ( 1 ) "*" "*" "*" "*" "*" " " "*"
## 79 ( 1 ) "*" "*" "*" "*" "*" "*" "*"
## 80 ( 1 ) "*" "*" "*" "*" "*" "*" "*"
## 81 ( 1 ) "*" "*" "*" "*" "*" "*" "*"
## SEMANA37 SEMANA38 SEMANA39 SEMANA40 SEMANA41 SEMANA42 SEMANA43
## 1 ( 1 ) " " " " " " " " " " " " " "
## 2 ( 1 ) " " " " " " " " " " " " " "
## 3 ( 1 ) " " " " " " " " " " " " " "
## 4 ( 1 ) " " " " " " " " " " " " " "
## 5 ( 1 ) " " " " " " " " " " " " " "
## 6 ( 1 ) " " " " " " " " " " " " " "
## 7 ( 1 ) " " " " " " " " " " " " " "
## 8 ( 1 ) " " " " " " " " " " " " " "
## 9 ( 1 ) " " " " " " " " " " " " " "
## 10 ( 1 ) " " " " " " " " " " " " " "
## 11 ( 1 ) " " " " " " " " " " " " " "
## 12 ( 1 ) " " " " " " " " " " " " " "
## 13 ( 1 ) " " " " " " " " " " " " " "
## 14 ( 1 ) " " " " " " " " "*" " " " "
## 15 ( 1 ) " " " " " " " " "*" " " " "
## 16 ( 1 ) " " " " " " " " "*" " " " "
## 17 ( 1 ) " " "*" " " " " "*" " " " "
## 18 ( 1 ) " " "*" " " " " "*" " " " "
## 19 ( 1 ) "*" "*" " " " " "*" " " " "
## 20 ( 1 ) "*" "*" " " "*" "*" " " " "
## 21 ( 1 ) "*" "*" " " "*" "*" " " " "
## 22 ( 1 ) "*" "*" " " "*" "*" " " " "
## 23 ( 1 ) "*" "*" " " "*" "*" " " " "
## 24 ( 1 ) "*" "*" " " "*" "*" " " " "
## 25 ( 1 ) "*" "*" " " "*" "*" " " " "
## 26 ( 1 ) "*" "*" " " "*" "*" " " " "
## 27 ( 1 ) "*" "*" " " "*" "*" " " " "
## 28 ( 1 ) "*" "*" " " "*" "*" " " " "
## 29 ( 1 ) "*" "*" " " "*" "*" " " " "
## 30 ( 1 ) "*" "*" " " "*" "*" " " " "
## 31 ( 1 ) "*" "*" " " "*" "*" " " " "
## 32 ( 1 ) "*" "*" " " "*" "*" " " " "
## 33 ( 1 ) "*" "*" " " "*" "*" " " " "
## 34 ( 1 ) "*" "*" " " "*" "*" " " " "
## 35 ( 1 ) "*" "*" " " "*" "*" " " " "
## 36 ( 1 ) "*" "*" " " "*" "*" " " " "
## 37 ( 1 ) "*" "*" " " "*" "*" " " " "
## 38 ( 1 ) "*" "*" " " "*" "*" " " " "
## 39 ( 1 ) "*" "*" " " "*" "*" " " " "
## 40 ( 1 ) "*" "*" " " "*" "*" " " " "
## 41 ( 1 ) "*" "*" " " "*" "*" " " " "
## 42 ( 1 ) "*" "*" " " "*" "*" " " " "
## 43 ( 1 ) "*" "*" " " "*" "*" " " " "
## 44 ( 1 ) "*" "*" " " "*" "*" " " " "
## 45 ( 1 ) "*" "*" " " "*" "*" " " " "
## 46 ( 1 ) "*" "*" " " "*" "*" " " " "
## 47 ( 1 ) "*" "*" " " "*" "*" " " " "
## 48 ( 1 ) "*" "*" " " "*" "*" " " " "
## 49 ( 1 ) "*" "*" " " "*" "*" " " " "
## 50 ( 1 ) "*" "*" " " "*" "*" " " " "
## 51 ( 1 ) "*" "*" " " "*" "*" " " " "
## 52 ( 1 ) "*" "*" " " "*" "*" " " " "
## 53 ( 1 ) "*" "*" " " "*" "*" " " " "
## 54 ( 1 ) "*" "*" " " "*" "*" " " " "
## 55 ( 1 ) "*" "*" " " "*" "*" " " " "
## 56 ( 1 ) "*" "*" " " "*" "*" " " " "
## 57 ( 1 ) "*" "*" " " "*" "*" " " " "
## 58 ( 1 ) "*" "*" " " "*" "*" " " " "
## 59 ( 1 ) "*" "*" " " "*" "*" " " " "
## 60 ( 1 ) "*" "*" " " "*" "*" " " " "
## 61 ( 1 ) "*" "*" " " "*" "*" " " " "
## 62 ( 1 ) "*" "*" "*" "*" "*" " " " "
## 63 ( 1 ) "*" "*" "*" "*" "*" " " " "
## 64 ( 1 ) "*" "*" "*" "*" "*" " " " "
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## 66 ( 1 ) "*" "*" "*" "*" "*" " " " "
## 67 ( 1 ) "*" "*" "*" "*" "*" " " " "
## 68 ( 1 ) "*" "*" "*" "*" "*" " " " "
## 69 ( 1 ) "*" "*" "*" "*" "*" " " " "
## 70 ( 1 ) "*" "*" "*" "*" "*" " " " "
## 71 ( 1 ) "*" "*" "*" "*" "*" " " " "
## 72 ( 1 ) "*" "*" "*" "*" "*" " " " "
## 73 ( 1 ) "*" "*" "*" "*" "*" " " " "
## 74 ( 1 ) "*" "*" "*" "*" "*" " " " "
## 75 ( 1 ) "*" "*" "*" "*" "*" " " " "
## 76 ( 1 ) "*" "*" "*" "*" "*" " " " "
## 77 ( 1 ) "*" "*" "*" "*" "*" " " " "
## 78 ( 1 ) "*" "*" "*" "*" "*" " " " "
## 79 ( 1 ) "*" "*" "*" "*" "*" " " " "
## 80 ( 1 ) "*" "*" "*" "*" "*" "*" " "
## 81 ( 1 ) "*" "*" "*" "*" "*" "*" "*"
## SEMANA44 SEMANA45 SEMANA46 SEMANA47 SEMANA48 SEMANA49 SEMANA50
## 1 ( 1 ) " " " " " " " " " " " " " "
## 2 ( 1 ) " " " " " " " " " " " " " "
## 3 ( 1 ) " " " " " " " " " " " " " "
## 4 ( 1 ) " " " " " " " " " " " " " "
## 5 ( 1 ) " " " " " " " " " " " " " "
## 6 ( 1 ) " " " " " " " " " " " " " "
## 7 ( 1 ) " " " " " " " " " " " " " "
## 8 ( 1 ) " " " " " " " " " " " " " "
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## 12 ( 1 ) " " " " " " " " " " " " " "
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## 14 ( 1 ) " " " " " " " " " " " " " "
## 15 ( 1 ) " " " " " " " " " " " " " "
## 16 ( 1 ) " " " " " " " " " " " " " "
## 17 ( 1 ) " " " " " " " " " " " " " "
## 18 ( 1 ) " " " " " " " " " " " " " "
## 19 ( 1 ) " " " " " " " " " " " " " "
## 20 ( 1 ) " " " " " " " " " " " " " "
## 21 ( 1 ) " " " " " " " " " " " " " "
## 22 ( 1 ) " " " " " " " " " " " " " "
## 23 ( 1 ) " " " " " " " " " " " " " "
## 24 ( 1 ) " " " " " " " " " " " " " "
## 25 ( 1 ) " " " " " " " " " " " " " "
## 26 ( 1 ) " " " " " " " " " " " " " "
## 27 ( 1 ) " " " " " " " " " " " " " "
## 28 ( 1 ) " " " " " " " " " " " " " "
## 29 ( 1 ) " " " " " " " " " " " " " "
## 30 ( 1 ) " " " " " " " " " " " " " "
## 31 ( 1 ) " " " " " " " " " " " " " "
## 32 ( 1 ) " " " " " " " " " " " " " "
## 33 ( 1 ) " " " " " " " " " " " " " "
## 34 ( 1 ) " " " " " " " " " " " " " "
## 35 ( 1 ) " " " " " " " " " " " " " "
## 36 ( 1 ) " " " " " " " " " " " " " "
## 37 ( 1 ) " " " " " " " " " " " " " "
## 38 ( 1 ) " " " " " " " " " " " " "*"
## 39 ( 1 ) " " " " " " " " " " "*" "*"
## 40 ( 1 ) " " " " " " " " " " "*" "*"
## 41 ( 1 ) " " " " " " " " " " "*" "*"
## 42 ( 1 ) " " " " " " " " " " "*" "*"
## 43 ( 1 ) " " " " " " " " " " "*" "*"
## 44 ( 1 ) " " " " " " " " " " "*" "*"
## 45 ( 1 ) " " " " " " " " " " "*" "*"
## 46 ( 1 ) " " " " " " " " " " "*" "*"
## 47 ( 1 ) " " " " " " " " " " "*" "*"
## 48 ( 1 ) " " "*" " " " " " " "*" "*"
## 49 ( 1 ) " " "*" " " " " " " "*" "*"
## 50 ( 1 ) " " "*" " " " " " " "*" "*"
## 51 ( 1 ) " " "*" " " " " " " "*" "*"
## 52 ( 1 ) " " "*" " " " " " " "*" "*"
## 53 ( 1 ) " " "*" " " " " " " "*" "*"
## 54 ( 1 ) " " "*" " " " " "*" "*" "*"
## 55 ( 1 ) " " "*" " " " " "*" "*" "*"
## 56 ( 1 ) " " "*" " " " " "*" "*" "*"
## 57 ( 1 ) " " "*" " " " " "*" "*" "*"
## 58 ( 1 ) " " "*" "*" " " "*" "*" "*"
## 59 ( 1 ) " " "*" "*" "*" "*" "*" "*"
## 60 ( 1 ) " " "*" "*" "*" "*" "*" "*"
## 61 ( 1 ) " " "*" "*" "*" "*" "*" "*"
## 62 ( 1 ) " " "*" "*" "*" "*" "*" "*"
## 63 ( 1 ) " " "*" "*" "*" "*" "*" "*"
## 64 ( 1 ) " " "*" "*" "*" "*" "*" "*"
## 65 ( 1 ) " " "*" "*" "*" "*" "*" "*"
## 66 ( 1 ) " " "*" "*" "*" "*" "*" "*"
## 67 ( 1 ) " " "*" "*" "*" "*" "*" "*"
## 68 ( 1 ) " " "*" "*" "*" "*" "*" "*"
## 69 ( 1 ) "*" "*" "*" "*" "*" "*" "*"
## 70 ( 1 ) "*" "*" "*" "*" "*" "*" "*"
## 71 ( 1 ) "*" "*" "*" "*" "*" "*" "*"
## 72 ( 1 ) "*" "*" "*" "*" "*" "*" "*"
## 73 ( 1 ) "*" "*" "*" "*" "*" "*" "*"
## 74 ( 1 ) "*" "*" "*" "*" "*" "*" "*"
## 75 ( 1 ) "*" "*" "*" "*" "*" "*" "*"
## 76 ( 1 ) "*" "*" "*" "*" "*" "*" "*"
## 77 ( 1 ) "*" "*" "*" "*" "*" "*" "*"
## 78 ( 1 ) "*" "*" "*" "*" "*" "*" "*"
## 79 ( 1 ) "*" "*" "*" "*" "*" "*" "*"
## 80 ( 1 ) "*" "*" "*" "*" "*" "*" "*"
## 81 ( 1 ) "*" "*" "*" "*" "*" "*" "*"
## SEMANA51 SEMANA52 SEMANA53 Feriado_Lunes Feriado_Otro
## 1 ( 1 ) " " " " " " " " " "
## 2 ( 1 ) " " " " " " "*" " "
## 3 ( 1 ) " " " " " " "*" "*"
## 4 ( 1 ) " " " " " " "*" "*"
## 5 ( 1 ) " " " " " " "*" "*"
## 6 ( 1 ) " " " " " " "*" "*"
## 7 ( 1 ) " " " " " " "*" "*"
## 8 ( 1 ) " " " " "*" "*" "*"
## 9 ( 1 ) " " "*" "*" "*" "*"
## 10 ( 1 ) " " "*" "*" "*" "*"
## 11 ( 1 ) " " "*" "*" "*" "*"
## 12 ( 1 ) " " "*" "*" "*" "*"
## 13 ( 1 ) " " "*" "*" "*" "*"
## 14 ( 1 ) " " "*" "*" "*" "*"
## 15 ( 1 ) " " "*" "*" "*" "*"
## 16 ( 1 ) "*" "*" "*" "*" "*"
## 17 ( 1 ) "*" "*" "*" "*" "*"
## 18 ( 1 ) "*" "*" "*" "*" "*"
## 19 ( 1 ) "*" "*" "*" "*" "*"
## 20 ( 1 ) "*" "*" "*" "*" "*"
## 21 ( 1 ) "*" "*" "*" "*" "*"
## 22 ( 1 ) "*" "*" "*" "*" "*"
## 23 ( 1 ) "*" "*" "*" "*" "*"
## 24 ( 1 ) "*" "*" "*" "*" "*"
## 25 ( 1 ) "*" "*" "*" "*" "*"
## 26 ( 1 ) "*" "*" "*" "*" "*"
## 27 ( 1 ) "*" "*" "*" "*" "*"
## 28 ( 1 ) "*" "*" "*" "*" "*"
## 29 ( 1 ) "*" "*" "*" "*" "*"
## 30 ( 1 ) "*" "*" "*" "*" "*"
## 31 ( 1 ) "*" "*" "*" "*" "*"
## 32 ( 1 ) "*" "*" "*" "*" "*"
## 33 ( 1 ) "*" "*" "*" "*" "*"
## 34 ( 1 ) "*" "*" "*" "*" "*"
## 35 ( 1 ) "*" "*" "*" "*" "*"
## 36 ( 1 ) "*" "*" "*" "*" "*"
## 37 ( 1 ) "*" "*" "*" "*" "*"
## 38 ( 1 ) "*" "*" "*" "*" "*"
## 39 ( 1 ) "*" "*" "*" "*" "*"
## 40 ( 1 ) "*" "*" "*" "*" "*"
## 41 ( 1 ) "*" "*" "*" "*" "*"
## 42 ( 1 ) "*" "*" "*" "*" "*"
## 43 ( 1 ) "*" "*" "*" "*" "*"
## 44 ( 1 ) "*" "*" "*" "*" "*"
## 45 ( 1 ) "*" "*" "*" "*" "*"
## 46 ( 1 ) "*" "*" "*" "*" "*"
## 47 ( 1 ) "*" "*" "*" "*" "*"
## 48 ( 1 ) "*" "*" "*" "*" "*"
## 49 ( 1 ) "*" "*" "*" "*" "*"
## 50 ( 1 ) "*" "*" "*" "*" "*"
## 51 ( 1 ) "*" "*" "*" "*" "*"
## 52 ( 1 ) "*" "*" "*" "*" "*"
## 53 ( 1 ) "*" "*" "*" "*" "*"
## 54 ( 1 ) "*" "*" "*" "*" "*"
## 55 ( 1 ) "*" "*" "*" "*" "*"
## 56 ( 1 ) "*" "*" "*" "*" "*"
## 57 ( 1 ) "*" "*" "*" "*" "*"
## 58 ( 1 ) "*" "*" "*" "*" "*"
## 59 ( 1 ) "*" "*" "*" "*" "*"
## 60 ( 1 ) "*" "*" "*" "*" "*"
## 61 ( 1 ) "*" "*" "*" "*" "*"
## 62 ( 1 ) "*" "*" "*" "*" "*"
## 63 ( 1 ) "*" "*" "*" "*" "*"
## 64 ( 1 ) "*" "*" "*" "*" "*"
## 65 ( 1 ) "*" "*" "*" "*" "*"
## 66 ( 1 ) "*" "*" "*" "*" "*"
## 67 ( 1 ) "*" "*" "*" "*" "*"
## 68 ( 1 ) "*" "*" "*" "*" "*"
## 69 ( 1 ) "*" "*" "*" "*" "*"
## 70 ( 1 ) "*" "*" "*" "*" "*"
## 71 ( 1 ) "*" "*" "*" "*" "*"
## 72 ( 1 ) "*" "*" "*" "*" "*"
## 73 ( 1 ) "*" "*" "*" "*" "*"
## 74 ( 1 ) "*" "*" "*" "*" "*"
## 75 ( 1 ) "*" "*" "*" "*" "*"
## 76 ( 1 ) "*" "*" "*" "*" "*"
## 77 ( 1 ) "*" "*" "*" "*" "*"
## 78 ( 1 ) "*" "*" "*" "*" "*"
## 79 ( 1 ) "*" "*" "*" "*" "*"
## 80 ( 1 ) "*" "*" "*" "*" "*"
## 81 ( 1 ) "*" "*" "*" "*" "*"
## Previo_feriado Prima Mujer Padre Madre AmoryAmistad Semana_Santa
## 1 ( 1 ) " " " " " " " " " " " " " "
## 2 ( 1 ) " " " " " " " " " " " " " "
## 3 ( 1 ) " " " " " " " " " " " " " "
## 4 ( 1 ) " " " " " " " " " " " " " "
## 5 ( 1 ) " " " " " " " " " " " " " "
## 6 ( 1 ) " " " " " " " " " " " " " "
## 7 ( 1 ) " " " " " " " " " " " " "*"
## 8 ( 1 ) " " " " " " " " " " " " "*"
## 9 ( 1 ) " " " " " " " " " " " " "*"
## 10 ( 1 ) " " " " " " " " " " " " "*"
## 11 ( 1 ) " " " " " " " " " " " " "*"
## 12 ( 1 ) " " " " " " " " " " " " "*"
## 13 ( 1 ) " " " " " " " " " " " " "*"
## 14 ( 1 ) " " " " " " " " " " " " "*"
## 15 ( 1 ) " " " " " " " " " " " " "*"
## 16 ( 1 ) " " " " " " " " " " " " "*"
## 17 ( 1 ) " " " " " " " " " " " " "*"
## 18 ( 1 ) " " " " " " " " "*" " " "*"
## 19 ( 1 ) " " " " " " " " "*" " " "*"
## 20 ( 1 ) " " " " " " " " "*" " " "*"
## 21 ( 1 ) " " " " " " " " "*" " " "*"
## 22 ( 1 ) " " " " " " " " "*" " " "*"
## 23 ( 1 ) " " " " " " " " "*" " " "*"
## 24 ( 1 ) " " " " " " " " "*" " " "*"
## 25 ( 1 ) " " " " " " " " "*" " " "*"
## 26 ( 1 ) " " " " " " " " "*" " " "*"
## 27 ( 1 ) " " " " " " " " "*" " " "*"
## 28 ( 1 ) " " " " " " " " "*" " " "*"
## 29 ( 1 ) " " " " " " " " "*" " " "*"
## 30 ( 1 ) " " " " " " " " "*" " " "*"
## 31 ( 1 ) " " " " " " " " "*" " " "*"
## 32 ( 1 ) " " " " " " " " "*" " " "*"
## 33 ( 1 ) " " "*" " " " " "*" " " "*"
## 34 ( 1 ) " " "*" " " " " "*" " " "*"
## 35 ( 1 ) " " "*" " " " " "*" " " "*"
## 36 ( 1 ) " " "*" " " " " "*" " " "*"
## 37 ( 1 ) " " "*" " " " " "*" " " "*"
## 38 ( 1 ) " " "*" " " " " "*" " " "*"
## 39 ( 1 ) " " "*" " " " " "*" " " "*"
## 40 ( 1 ) " " "*" " " " " "*" " " "*"
## 41 ( 1 ) " " "*" " " " " "*" " " "*"
## 42 ( 1 ) " " "*" " " " " "*" " " "*"
## 43 ( 1 ) " " "*" " " " " "*" " " "*"
## 44 ( 1 ) " " "*" " " "*" "*" " " "*"
## 45 ( 1 ) " " "*" " " "*" "*" " " "*"
## 46 ( 1 ) " " "*" " " "*" "*" " " "*"
## 47 ( 1 ) " " "*" " " "*" "*" " " "*"
## 48 ( 1 ) " " "*" " " "*" "*" " " "*"
## 49 ( 1 ) " " "*" " " "*" "*" " " "*"
## 50 ( 1 ) " " "*" " " "*" "*" " " "*"
## 51 ( 1 ) " " "*" " " "*" "*" " " "*"
## 52 ( 1 ) " " "*" " " "*" "*" " " "*"
## 53 ( 1 ) " " "*" "*" "*" "*" " " "*"
## 54 ( 1 ) " " "*" "*" "*" "*" " " "*"
## 55 ( 1 ) " " "*" "*" "*" "*" " " "*"
## 56 ( 1 ) " " "*" "*" "*" "*" " " "*"
## 57 ( 1 ) " " "*" "*" "*" "*" " " "*"
## 58 ( 1 ) " " "*" "*" "*" "*" " " "*"
## 59 ( 1 ) " " "*" "*" "*" "*" " " "*"
## 60 ( 1 ) " " "*" "*" "*" "*" " " "*"
## 61 ( 1 ) " " "*" "*" "*" "*" " " "*"
## 62 ( 1 ) " " "*" "*" "*" "*" " " "*"
## 63 ( 1 ) " " "*" "*" "*" "*" " " "*"
## 64 ( 1 ) " " "*" "*" "*" "*" " " "*"
## 65 ( 1 ) " " "*" "*" "*" "*" " " "*"
## 66 ( 1 ) " " "*" "*" "*" "*" " " "*"
## 67 ( 1 ) " " "*" "*" "*" "*" " " "*"
## 68 ( 1 ) " " "*" "*" "*" "*" " " "*"
## 69 ( 1 ) " " "*" "*" "*" "*" " " "*"
## 70 ( 1 ) " " "*" "*" "*" "*" " " "*"
## 71 ( 1 ) " " "*" "*" "*" "*" " " "*"
## 72 ( 1 ) " " "*" "*" "*" "*" " " "*"
## 73 ( 1 ) " " "*" "*" "*" "*" " " "*"
## 74 ( 1 ) " " "*" "*" "*" "*" " " "*"
## 75 ( 1 ) " " "*" "*" "*" "*" " " "*"
## 76 ( 1 ) " " "*" "*" "*" "*" " " "*"
## 77 ( 1 ) " " "*" "*" "*" "*" " " "*"
## 78 ( 1 ) " " "*" "*" "*" "*" " " "*"
## 79 ( 1 ) " " "*" "*" "*" "*" " " "*"
## 80 ( 1 ) " " "*" "*" "*" "*" " " "*"
## 81 ( 1 ) " " "*" "*" "*" "*" " " "*"
## Viernes_Antes_Puente Quincena Viernes_Desp_Quincena_v1
## 1 ( 1 ) " " " " " "
## 2 ( 1 ) " " " " " "
## 3 ( 1 ) " " " " " "
## 4 ( 1 ) " " " " " "
## 5 ( 1 ) " " " " " "
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## 24 ( 1 ) " " " " " "
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## 28 ( 1 ) " " " " " "
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## 32 ( 1 ) " " " " " "
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## 48 ( 1 ) " " " " " "
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## 54 ( 1 ) " " " " " "
## 55 ( 1 ) " " " " " "
## 56 ( 1 ) " " " " " "
## 57 ( 1 ) " " " " " "
## 58 ( 1 ) " " " " " "
## 59 ( 1 ) " " " " " "
## 60 ( 1 ) "*" " " " "
## 61 ( 1 ) "*" " " " "
## 62 ( 1 ) "*" " " " "
## 63 ( 1 ) "*" " " " "
## 64 ( 1 ) "*" " " " "
## 65 ( 1 ) "*" " " " "
## 66 ( 1 ) "*" " " " "
## 67 ( 1 ) "*" " " " "
## 68 ( 1 ) "*" " " " "
## 69 ( 1 ) "*" " " " "
## 70 ( 1 ) "*" "*" " "
## 71 ( 1 ) "*" "*" " "
## 72 ( 1 ) "*" "*" " "
## 73 ( 1 ) "*" "*" " "
## 74 ( 1 ) "*" "*" " "
## 75 ( 1 ) "*" "*" " "
## 76 ( 1 ) "*" "*" " "
## 77 ( 1 ) "*" "*" " "
## 78 ( 1 ) "*" "*" " "
## 79 ( 1 ) "*" "*" " "
## 80 ( 1 ) "*" "*" " "
## 81 ( 1 ) "*" "*" " "
## Viernes_Desp_Quincena_v2 Feria_Flores
## 1 ( 1 ) " " " "
## 2 ( 1 ) " " " "
## 3 ( 1 ) " " " "
## 4 ( 1 ) " " " "
## 5 ( 1 ) " " " "
## 6 ( 1 ) " " "*"
## 7 ( 1 ) " " "*"
## 8 ( 1 ) " " "*"
## 9 ( 1 ) " " "*"
## 10 ( 1 ) " " "*"
## 11 ( 1 ) "*" "*"
## 12 ( 1 ) "*" "*"
## 13 ( 1 ) "*" "*"
## 14 ( 1 ) "*" "*"
## 15 ( 1 ) "*" "*"
## 16 ( 1 ) "*" "*"
## 17 ( 1 ) "*" "*"
## 18 ( 1 ) "*" "*"
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## 20 ( 1 ) "*" "*"
## 21 ( 1 ) "*" "*"
## 22 ( 1 ) "*" "*"
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## 24 ( 1 ) "*" "*"
## 25 ( 1 ) "*" "*"
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## 30 ( 1 ) "*" "*"
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## 78 ( 1 ) "*" "*"
## 79 ( 1 ) "*" "*"
## 80 ( 1 ) "*" "*"
## 81 ( 1 ) "*" "*"reg.summary =summary(regfit.fwd)
names(reg.summary)## [1] "which" "rsq" "rss" "adjr2" "cp" "bic" "outmat" "obj"reg.summary$rsq## [1] 0.3619356 0.4868724 0.5461869 0.5647899 0.5811803 0.5914461 0.6001406
## [8] 0.6072893 0.6127937 0.6168722 0.6205206 0.6232782 0.6257787 0.6276244
## [15] 0.6291780 0.6305914 0.6319706 0.6332380 0.6345182 0.6357928 0.6371520
## [22] 0.6384302 0.6395528 0.6407111 0.6417461 0.6428253 0.6438054 0.6447313
## [29] 0.6458419 0.6470126 0.6481664 0.6501191 0.6575072 0.6658274 0.6673136
## [36] 0.6680038 0.6694037 0.6700405 0.6707762 0.6727615 0.6733687 0.6742044
## [43] 0.6747126 0.6752040 0.6756863 0.6761555 0.6765963 0.6770235 0.6773791
## [50] 0.6777361 0.6780563 0.6784142 0.6787212 0.6789833 0.6792370 0.6795436
## [57] 0.6797373 0.6799206 0.6802358 0.6804313 0.6805933 0.6807395 0.6808583
## [64] 0.6812809 0.6814133 0.6815839 0.6817376 0.6819732 0.6821051 0.6821782
## [71] 0.6822508 0.6823226 0.6823917 0.6824408 0.6827324 0.6829369 0.6834268
## [78] 0.6836086 0.6846287 0.6847462 0.6866739Selección de variables con el mejor R^2 ajustado
max_adjr<-which.max (reg.summary$adjr2)
max_adjr## [1] 81par(mfrow =c(2,2))
plot(reg.summary$rss ,xlab=" Number of Variables ",ylab=" RSS",
type="l")
plot(reg.summary$adjr2 ,xlab =" Number of Variables ",
ylab=" Adjusted RSq",type="l")
points (max_adjr, reg.summary$adjr2[max_adjr], col ="red",cex =2, pch =20)
plot(regfit.fwd ,scale ="adjr2")
coef(regfit.fwd ,max_adjr)## (Intercept) ANO2015 ANO2016
## 91.5331404 1.4597935 3.3094593
## ANO2017 MES02 MES03
## 2.8862101 10.1318470 8.3480093
## MES04 MES05 MES06
## -1.8588087 0.8584357 -4.4074025
## MES07 MES08 MES09
## -6.7741762 0.5131129 5.7563074
## MES10 MES11 DIA3
## 7.5011095 5.8601290 -3.0082054
## DIA4 DIA5 DIA6
## -2.8401209 1.9100357 -10.7965815
## DIA7 SEMANA02 SEMANA03
## -49.2320369 9.9391395 25.2484541
## SEMANA04 SEMANA05 SEMANA06
## 25.7372255 24.8610857 25.0235625
## SEMANA07 SEMANA08 SEMANA09
## 27.5602585 24.3782785 24.5568933
## SEMANA10 SEMANA11 SEMANA12
## 32.0312685 29.9689863 21.1349492
## SEMANA13 SEMANA14 SEMANA15
## 28.4389418 34.7949816 34.9648516
## SEMANA16 SEMANA17 SEMANA18
## 27.5404099 42.8269575 39.0298950
## SEMANA19 SEMANA20 SEMANA21
## 35.3718898 36.7380164 35.6442715
## SEMANA22 SEMANA23 SEMANA24
## 32.6505866 42.3392837 37.5205753
## SEMANA25 SEMANA26 SEMANA27
## 32.0955776 26.5140433 38.8450921
## SEMANA28 SEMANA29 SEMANA30
## 40.8407908 44.7709308 44.0765940
## SEMANA31 SEMANA32 SEMANA33
## 45.7860746 41.0110605 38.5148798
## SEMANA34 SEMANA35 SEMANA36
## 33.4166622 36.5006637 29.4490355
## SEMANA37 SEMANA38 SEMANA39
## 36.3051582 36.3051582 27.4090784
## SEMANA40 SEMANA41 SEMANA42
## 34.3032214 19.3534264 28.2717101
## SEMANA43 SEMANA44 SEMANA45
## 27.8328714 27.0802680 23.9883723
## SEMANA46 SEMANA47 SEMANA48
## 30.0088245 28.5590973 28.9610670
## SEMANA49 SEMANA50 SEMANA51
## 35.1462102 36.5252278 41.5081224
## SEMANA52 SEMANA53 Feriado_Lunes
## 20.0776865 1.7627823 -55.2274120
## Feriado_Otro Mujer Padre
## -46.1437891 7.1829221 10.7271739
## Madre AmoryAmistad Viernes_Antes_Puente
## 16.1402040 1.5381099 3.2515389
## Quincena Viernes_Desp_Quincena_v1 Feria_Flores
## -1.2908487 2.0679537 6.5670045
## Prima
## 0.87364564. Modelamiento
set.seed(123) # fija la semilla del generador de parámetros para que sea reproducibleSe realiza el modelo de regresión lineal con las variables seleccionadas y se revisa el p-valor de cada una para seleccionar las variables definitivas del modelo
library(caret)## Loading required package: lattice##
## Attaching package: 'caret'## The following object is masked from 'package:purrr':
##
## lifttrcntrl = trainControl(method="cv", number=10)
caret_lm_fit = caret::train(TOTAL_ACCIDENTES∼ANO+MES+DIA+SEMANA+Feriado_Lunes+Feriado_Otro+Previo_feriado+Mujer+Padre+Madre+AmoryAmistad+Semana_Santa+Viernes_Antes_Puente+Quincena+Viernes_Desp_Quincena_v1+Viernes_Desp_Quincena_v2+Feria_Flores, data=Train_D_Dataset,
method = "lm", trControl = trcntrl,
preProcess=c("center", "scale"),
tuneLength = 10)summary(caret_lm_fit)##
## Call:
## lm(formula = .outcome ~ ., data = dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -59.655 -9.817 -0.301 8.364 60.901
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 115.72758 0.39372 293.937 < 2e-16 ***
## ANO2015 0.63790 0.48369 1.319 0.187447
## ANO2016 1.31357 0.49048 2.678 0.007492 **
## ANO2017 1.15567 0.49042 2.356 0.018589 *
## MES02 2.81047 1.46850 1.914 0.055849 .
## MES03 2.49379 2.07802 1.200 0.230314
## MES04 -1.36187 2.40636 -0.566 0.571523
## MES05 -0.68034 2.69979 -0.252 0.801082
## MES06 -1.73599 2.79289 -0.622 0.534325
## MES07 -2.43242 2.89557 -0.840 0.401027
## MES08 -0.15743 2.86236 -0.055 0.956146
## MES09 1.36931 2.69708 0.508 0.611745
## MES10 2.11948 2.54224 0.834 0.404591
## MES11 1.76505 2.21645 0.796 0.425970
## MES12 0.47388 1.74247 0.272 0.785697
## DIA2 -0.14108 0.55304 -0.255 0.798680
## DIA3 -1.13362 0.55359 -2.048 0.040773 *
## DIA4 -1.14256 0.55587 -2.055 0.040025 *
## DIA5 0.56261 0.66007 0.852 0.394163
## DIA6 -3.65859 0.57668 -6.344 3.03e-10 ***
## DIA7 -17.16045 0.57897 -29.639 < 2e-16 ***
## SEMANA02 1.39394 0.57673 2.417 0.015779 *
## SEMANA03 3.50892 0.57956 6.054 1.81e-09 ***
## SEMANA04 3.56673 0.57827 6.168 9.08e-10 ***
## SEMANA05 3.41426 0.69869 4.887 1.15e-06 ***
## SEMANA06 3.41765 0.93976 3.637 0.000286 ***
## SEMANA07 3.73395 0.94062 3.970 7.57e-05 ***
## SEMANA08 3.34095 0.94026 3.553 0.000393 ***
## SEMANA09 3.31931 0.98631 3.365 0.000785 ***
## SEMANA10 4.35198 1.16860 3.724 0.000204 ***
## SEMANA11 4.24921 1.15801 3.669 0.000252 ***
## SEMANA12 3.52306 1.16096 3.035 0.002454 **
## SEMANA13 4.11533 1.14986 3.579 0.000357 ***
## SEMANA14 6.03720 1.28650 4.693 2.96e-06 ***
## SEMANA15 6.09122 1.32362 4.602 4.57e-06 ***
## SEMANA16 4.90002 1.32069 3.710 0.000215 ***
## SEMANA17 6.31946 1.30736 4.834 1.49e-06 ***
## SEMANA18 5.80538 1.35362 4.289 1.92e-05 ***
## SEMANA19 5.34226 1.42694 3.744 0.000189 ***
## SEMANA20 5.53195 1.42696 3.877 0.000111 ***
## SEMANA21 5.39685 1.42613 3.784 0.000161 ***
## SEMANA22 4.92935 1.39804 3.526 0.000436 ***
## SEMANA23 6.23588 1.48311 4.205 2.78e-05 ***
## SEMANA24 5.55857 1.48855 3.734 0.000196 ***
## SEMANA25 4.86318 1.48420 3.277 0.001077 **
## SEMANA26 4.04286 1.46048 2.768 0.005713 **
## SEMANA27 5.66544 1.49034 3.801 0.000150 ***
## SEMANA28 5.89159 1.50894 3.904 9.90e-05 ***
## SEMANA29 6.38997 1.50698 4.240 2.38e-05 ***
## SEMANA30 6.37292 1.53198 4.160 3.38e-05 ***
## SEMANA31 6.56533 1.47688 4.445 9.48e-06 ***
## SEMANA32 5.82592 1.47639 3.946 8.35e-05 ***
## SEMANA33 5.47489 1.48580 3.685 0.000238 ***
## SEMANA34 4.76880 1.48956 3.201 0.001398 **
## SEMANA35 5.15192 1.43139 3.599 0.000330 ***
## SEMANA36 4.20665 1.41636 2.970 0.003029 **
## SEMANA37 5.09974 1.42352 3.582 0.000352 ***
## SEMANA38 5.14839 1.42368 3.616 0.000310 ***
## SEMANA39 3.87478 1.39122 2.785 0.005423 **
## SEMANA40 4.77100 1.30836 3.647 0.000276 ***
## SEMANA41 2.71063 1.32334 2.048 0.040718 *
## SEMANA42 3.90902 1.32514 2.950 0.003233 **
## SEMANA43 3.84189 1.32240 2.905 0.003728 **
## SEMANA44 3.72198 1.19699 3.109 0.001913 **
## SEMANA45 3.26752 1.18352 2.761 0.005842 **
## SEMANA46 4.06205 1.18385 3.431 0.000619 ***
## SEMANA47 3.88913 1.18593 3.279 0.001066 **
## SEMANA48 3.89400 1.07740 3.614 0.000312 ***
## SEMANA49 4.72884 0.94379 5.010 6.14e-07 ***
## SEMANA50 4.88178 0.94594 5.161 2.82e-07 ***
## SEMANA51 5.62281 0.94443 5.954 3.32e-09 ***
## SEMANA52 2.61486 0.92316 2.833 0.004686 **
## SEMANA53 0.03037 0.48919 0.062 0.950512
## Feriado_Lunes -9.43456 0.45770 -20.613 < 2e-16 ***
## Feriado_Otro -5.54755 0.43090 -12.874 < 2e-16 ***
## Previo_feriado -0.17094 0.56717 -0.301 0.763158
## Mujer 0.36942 0.42549 0.868 0.385419
## Padre 0.54438 0.41641 1.307 0.191323
## Madre 0.83691 0.41672 2.008 0.044806 *
## AmoryAmistad 0.05610 0.41230 0.136 0.891790
## Semana_Santa -3.10987 0.47057 -6.609 5.53e-11 ***
## Viernes_Antes_Puente 0.57704 0.53443 1.080 0.280452
## Quincena -0.23694 0.40341 -0.587 0.557066
## Viernes_Desp_Quincena_v1 -0.07675 0.65663 -0.117 0.906969
## Viernes_Desp_Quincena_v2 0.82050 0.59789 1.372 0.170185
## Feria_Flores 0.94102 0.67190 1.401 0.161578
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 15.05 on 1375 degrees of freedom
## Multiple R-squared: 0.6867, Adjusted R-squared: 0.6674
## F-statistic: 35.46 on 85 and 1375 DF, p-value: < 2.2e-161. REGRESION LINEAL
Total Accidentes
head(Train_D_Dataset)## FECHA ACCIDENTES_GRAVES ACCIDENTES_LEVES TOTAL_ACCIDENTES Ano_Base
## 1 2014-01-01 56 18 74 0
## 2 2014-01-02 42 30 72 0
## 3 2014-01-03 51 42 93 0
## 4 2014-01-04 41 27 68 0
## 5 2014-01-05 36 31 67 0
## 6 2014-01-06 29 14 43 0
## Lunes martes miercoles jueves viernes sabado domingo Enero Febrero Marzo
## 1 0 0 1 0 0 0 0 1 0 0
## 2 0 0 0 1 0 0 0 1 0 0
## 3 0 0 0 0 1 0 0 1 0 0
## 4 0 0 0 0 0 1 0 1 0 0
## 5 0 0 0 0 0 0 1 1 0 0
## 6 1 0 0 0 0 0 0 1 0 0
## Abril Mayo Junio Julio Agosto Septiembre Octubre Noviembre Diciembre
## 1 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0
## Feriado Feriado_v1 Feriado_Lunes Feriado_Otro Previo_feriado
## 1 1 1 0 1 0
## 2 0 0 0 0 0
## 3 0 0 0 0 1
## 4 0 0 0 0 1
## 5 0 0 0 0 1
## 6 1 1 1 0 0
## Semana_Santa Semana_Santa_Mes Semana_Santa_Semana Prima Mujer Padre
## 1 0 0 0 0 0 0
## 2 0 0 0 0 0 0
## 3 0 0 0 0 0 0
## 4 0 0 0 0 0 0
## 5 0 0 0 0 0 0
## 6 0 0 0 0 0 0
## Madre AmoryAmistad Semana_Santa_v1 Viernes_Antes_Puente Quincena
## 1 0 0 0 0 0
## 2 0 0 0 0 0
## 3 0 0 0 1 0
## 4 0 0 0 0 0
## 5 0 0 0 0 0
## 6 0 0 0 0 0
## Viernes_Desp_Quincena Viernes_Desp_Quincena_v1 Viernes_Desp_Quincena_v2
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Feria_Flores Feria_Flores_Mes Feria_Flores_Semana ANO SEMANA MES DIA
## 1 0 0 0 2014 01 01 3
## 2 0 0 0 2014 01 01 4
## 3 0 0 0 2014 01 01 5
## 4 0 0 0 2014 01 01 6
## 5 0 0 0 2014 01 01 7
## 6 0 0 0 2014 02 01 1library(caret)
trcntrl = trainControl(method="cv", number=10)
caret_lm_fit = caret::train(TOTAL_ACCIDENTES∼Ano_Base+DIA+SEMANA+Feriado_Lunes+Feriado_Otro+Madre+Semana_Santa+Viernes_Desp_Quincena_v2+Feria_Flores, data=Train_D_Dataset,
method = "lm", trControl = trcntrl,
preProcess=c("center", "scale"),
tuneLength = 10)summary(caret_lm_fit)##
## Call:
## lm(formula = .outcome ~ ., data = dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -59.015 -9.875 -0.302 8.281 65.351
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 115.7276 0.3936 294.013 < 2e-16 ***
## Ano_Base 1.0776 0.4011 2.686 0.00731 **
## DIA2 -0.1253 0.5518 -0.227 0.82039
## DIA3 -1.1352 0.5524 -2.055 0.04006 *
## DIA4 -1.1460 0.5549 -2.065 0.03909 *
## DIA5 0.7339 0.5842 1.256 0.20927
## DIA6 -3.7236 0.5532 -6.731 2.45e-11 ***
## DIA7 -17.1487 0.5562 -30.834 < 2e-16 ***
## SEMANA02 1.3550 0.5644 2.401 0.01649 *
## SEMANA03 3.4624 0.5633 6.147 1.03e-09 ***
## SEMANA04 3.5289 0.5630 6.268 4.86e-10 ***
## SEMANA05 4.1389 0.5632 7.349 3.38e-13 ***
## SEMANA06 4.8270 0.5630 8.574 < 2e-16 ***
## SEMANA07 5.1260 0.5637 9.094 < 2e-16 ***
## SEMANA08 4.7486 0.5630 8.435 < 2e-16 ***
## SEMANA09 4.6019 0.5637 8.164 7.17e-16 ***
## SEMANA10 5.6842 0.5630 10.096 < 2e-16 ***
## SEMANA11 5.4337 0.5648 9.620 < 2e-16 ***
## SEMANA12 4.7082 0.5720 8.231 4.21e-16 ***
## SEMANA13 4.9672 0.5653 8.786 < 2e-16 ***
## SEMANA14 5.5068 0.5777 9.533 < 2e-16 ***
## SEMANA15 5.3599 0.5777 9.278 < 2e-16 ***
## SEMANA16 4.1558 0.5716 7.271 5.94e-13 ***
## SEMANA17 5.6184 0.5623 9.992 < 2e-16 ***
## SEMANA18 5.3485 0.5637 9.488 < 2e-16 ***
## SEMANA19 4.9672 0.5751 8.637 < 2e-16 ***
## SEMANA20 5.1554 0.5637 9.146 < 2e-16 ***
## SEMANA21 5.0419 0.5631 8.954 < 2e-16 ***
## SEMANA22 4.3657 0.5642 7.738 1.93e-14 ***
## SEMANA23 5.3402 0.5635 9.477 < 2e-16 ***
## SEMANA24 4.8120 0.5638 8.535 < 2e-16 ***
## SEMANA25 4.0177 0.5635 7.130 1.61e-12 ***
## SEMANA26 3.1019 0.5642 5.498 4.56e-08 ***
## SEMANA27 4.4525 0.5652 7.877 6.67e-15 ***
## SEMANA28 4.6532 0.5632 8.262 3.28e-16 ***
## SEMANA29 5.1612 0.5624 9.177 < 2e-16 ***
## SEMANA30 5.0680 0.5736 8.836 < 2e-16 ***
## SEMANA31 5.8182 0.6663 8.732 < 2e-16 ***
## SEMANA32 5.5639 0.6149 9.048 < 2e-16 ***
## SEMANA33 5.3655 0.5638 9.517 < 2e-16 ***
## SEMANA34 4.6539 0.5642 8.248 3.67e-16 ***
## SEMANA35 5.2191 0.5637 9.259 < 2e-16 ***
## SEMANA36 4.8221 0.5630 8.565 < 2e-16 ***
## SEMANA37 5.7530 0.5637 10.206 < 2e-16 ***
## SEMANA38 5.7968 0.5630 10.296 < 2e-16 ***
## SEMANA39 4.5552 0.5632 8.088 1.30e-15 ***
## SEMANA40 5.7038 0.5630 10.131 < 2e-16 ***
## SEMANA41 3.7396 0.5630 6.642 4.41e-11 ***
## SEMANA42 4.9008 0.5654 8.668 < 2e-16 ***
## SEMANA43 4.8515 0.5630 8.617 < 2e-16 ***
## SEMANA44 4.6606 0.5637 8.268 3.13e-16 ***
## SEMANA45 4.1243 0.5652 7.297 4.93e-13 ***
## SEMANA46 4.9045 0.5642 8.693 < 2e-16 ***
## SEMANA47 4.7328 0.5635 8.399 < 2e-16 ***
## SEMANA48 4.5676 0.5637 8.103 1.16e-15 ***
## SEMANA49 4.9550 0.5621 8.816 < 2e-16 ***
## SEMANA50 5.0744 0.5631 9.011 < 2e-16 ***
## SEMANA51 5.8192 0.5623 10.349 < 2e-16 ***
## SEMANA52 2.8069 0.5623 4.992 6.74e-07 ***
## SEMANA53 0.1113 0.4437 0.251 0.80197
## Feriado_Lunes -9.3842 0.4537 -20.684 < 2e-16 ***
## Feriado_Otro -5.6005 0.4222 -13.264 < 2e-16 ***
## Madre 0.8407 0.4158 2.022 0.04339 *
## Semana_Santa -3.0095 0.4672 -6.442 1.62e-10 ***
## Viernes_Desp_Quincena_v2 0.7397 0.4492 1.646 0.09989 .
## Feria_Flores 1.3243 0.5918 2.238 0.02539 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 15.05 on 1395 degrees of freedom
## Multiple R-squared: 0.6823, Adjusted R-squared: 0.6675
## F-statistic: 46.1 on 65 and 1395 DF, p-value: < 2.2e-16caret_lm_fit## Linear Regression
##
## 1461 samples
## 9 predictor
##
## Pre-processing: centered (65), scaled (65)
## Resampling: Cross-Validated (10 fold)
## Summary of sample sizes: 1315, 1315, 1315, 1314, 1315, 1314, ...
## Resampling results:
##
## RMSE Rsquared MAE
## 15.34565 0.6537082 11.93395
##
## Tuning parameter 'intercept' was held constant at a value of TRUECalculo MSE y RMSE para los datos de entrenamiento
Ano_Base+DIA+SEMANA+Feriado_Lunes+Feriado_Otro+Madre+Semana_Santa+Viernes_Desp_Quincena_v2+Feria_Flores
y_tr_pred_lm<-predict(caret_lm_fit,Train_D_Dataset[,c("Ano_Base","DIA","SEMANA","Feriado_Lunes","Feriado_Otro","Madre","Semana_Santa","Viernes_Desp_Quincena_v2","Feria_Flores")])mse_tr_lm<-mean((Train_D_Dataset$TOTAL_ACCIDENTES-y_tr_pred_lm)^2) # calcula el mse de entrenamiento
RMSE_tr_lm = sqrt(mse_tr_lm)
mse_tr_lm## [1] 216.1297RMSE_tr_lm## [1] 14.70135Calculo MSE y RMSE para los datos de validación
y_test_pred_lm<-predict(caret_lm_fit,Test_D_Dataset[,c("Ano_Base","DIA","SEMANA","Feriado_Lunes","Feriado_Otro","Madre","Semana_Santa","Viernes_Desp_Quincena_v2","Feria_Flores")])
mse_test_lm<-mean((Test_D_Dataset$TOTAL_ACCIDENTES-y_test_pred_lm)^2) # calcula el mse de entrenamiento
RMSE_test_lm = sqrt(mse_test_lm)
mse_test_lm## [1] 256.8317RMSE_test_lm## [1] 16.02597Predicción en la muestra
library(plotly)##
## Attaching package: 'plotly'## The following object is masked from 'package:reshape':
##
## rename## The following object is masked from 'package:ggplot2':
##
## last_plot## The following object is masked from 'package:stats':
##
## filter## The following object is masked from 'package:graphics':
##
## layoutplot_ly (data=Train_D_Dataset,
x = ~FECHA,
y = ~TOTAL_ACCIDENTES,
type = "scatter" ,mode = "lines",
name='Real',
line=list(width=1,color='rgb(205, 12, 24)'))%>%
add_trace(y= ~y_tr_pred_lm,
name='Modelo lm',
line=list(width=1,color='rgb(22, 96, 167)'))%>%
layout(title='Total accidentes',
xaxis=list(title="Fecha"),
yaxis=list(title="Accidentes"),
legend = list(x = 0.75, y = 0.9))Gráfica serie 2018
plot_ly (data=Test_D_Dataset,
x = ~FECHA,
y = ~TOTAL_ACCIDENTES,
type = "scatter" ,mode = "lines",
name='Real',
line=list(width=1,color='rgb(205, 12, 24)'))%>%
add_trace(y= ~y_test_pred_lm,
name='Modelo lm',
line=list(width=1,color='rgb(22, 96, 167)'))%>%
layout(title='Total accidentes',
xaxis=list(title="Fecha"),
yaxis=list(title="Accidentes"),
legend = list(x = 0.75, y = 0.9))Accidentes Graves
trcntrl = trainControl(method="cv", number=10)
caret_lm_fit_m = caret::train(ACCIDENTES_GRAVES∼Ano_Base+DIA+SEMANA+Feriado_Lunes+Feriado_Otro+Madre+Semana_Santa+Viernes_Desp_Quincena_v2+Feria_Flores, data=Train_D_Dataset,
method = "lm", trControl = trcntrl,
preProcess=c("center", "scale"),
tuneLength = 10)summary(caret_lm_fit_m)##
## Call:
## lm(formula = .outcome ~ ., data = dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -29.636 -6.537 -0.422 6.278 47.122
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 64.4606 0.2628 245.282 < 2e-16 ***
## Ano_Base 0.2202 0.2678 0.822 0.411017
## DIA2 -0.4368 0.3684 -1.186 0.235968
## DIA3 -0.4399 0.3688 -1.193 0.233148
## DIA4 -0.2523 0.3705 -0.681 0.496022
## DIA5 -0.4151 0.3901 -1.064 0.287403
## DIA6 -1.3858 0.3694 -3.752 0.000183 ***
## DIA7 -6.2262 0.3713 -16.767 < 2e-16 ***
## SEMANA02 0.2058 0.3768 0.546 0.585120
## SEMANA03 1.5883 0.3761 4.223 2.57e-05 ***
## SEMANA04 1.2446 0.3759 3.311 0.000953 ***
## SEMANA05 1.9048 0.3760 5.066 4.61e-07 ***
## SEMANA06 2.4447 0.3759 6.504 1.09e-10 ***
## SEMANA07 2.0164 0.3763 5.358 9.85e-08 ***
## SEMANA08 2.2830 0.3759 6.074 1.61e-09 ***
## SEMANA09 2.0311 0.3763 5.397 7.96e-08 ***
## SEMANA10 2.6406 0.3759 7.025 3.34e-12 ***
## SEMANA11 2.6676 0.3771 7.074 2.38e-12 ***
## SEMANA12 2.1047 0.3819 5.511 4.24e-08 ***
## SEMANA13 2.3333 0.3775 6.182 8.31e-10 ***
## SEMANA14 2.4926 0.3857 6.463 1.42e-10 ***
## SEMANA15 2.1595 0.3857 5.599 2.59e-08 ***
## SEMANA16 1.9295 0.3816 5.056 4.85e-07 ***
## SEMANA17 2.3504 0.3754 6.260 5.10e-10 ***
## SEMANA18 2.1095 0.3764 5.605 2.51e-08 ***
## SEMANA19 2.1563 0.3840 5.616 2.36e-08 ***
## SEMANA20 2.1927 0.3763 5.826 7.03e-09 ***
## SEMANA21 2.2506 0.3760 5.986 2.73e-09 ***
## SEMANA22 1.9270 0.3767 5.116 3.56e-07 ***
## SEMANA23 2.5660 0.3762 6.820 1.35e-11 ***
## SEMANA24 2.1456 0.3764 5.700 1.46e-08 ***
## SEMANA25 1.7088 0.3762 4.542 6.06e-06 ***
## SEMANA26 1.0453 0.3767 2.775 0.005593 **
## SEMANA27 1.9672 0.3774 5.213 2.14e-07 ***
## SEMANA28 2.1154 0.3760 5.626 2.23e-08 ***
## SEMANA29 2.2439 0.3755 5.976 2.90e-09 ***
## SEMANA30 2.2346 0.3829 5.835 6.67e-09 ***
## SEMANA31 2.7308 0.4449 6.139 1.08e-09 ***
## SEMANA32 2.3822 0.4106 5.802 8.08e-09 ***
## SEMANA33 2.4542 0.3764 6.520 9.81e-11 ***
## SEMANA34 2.2249 0.3767 5.906 4.39e-09 ***
## SEMANA35 2.8197 0.3763 7.492 1.20e-13 ***
## SEMANA36 2.0920 0.3759 5.565 3.13e-08 ***
## SEMANA37 2.7217 0.3763 7.232 7.82e-13 ***
## SEMANA38 2.6210 0.3759 6.973 4.78e-12 ***
## SEMANA39 2.2134 0.3760 5.886 4.94e-09 ***
## SEMANA40 2.4496 0.3759 6.517 1.00e-10 ***
## SEMANA41 1.2936 0.3759 3.441 0.000596 ***
## SEMANA42 2.1767 0.3775 5.766 9.97e-09 ***
## SEMANA43 2.0969 0.3759 5.579 2.91e-08 ***
## SEMANA44 1.7862 0.3763 4.746 2.29e-06 ***
## SEMANA45 1.7223 0.3774 4.564 5.47e-06 ***
## SEMANA46 1.7850 0.3767 4.739 2.37e-06 ***
## SEMANA47 1.8165 0.3762 4.828 1.53e-06 ***
## SEMANA48 1.6098 0.3763 4.278 2.02e-05 ***
## SEMANA49 1.5801 0.3753 4.211 2.71e-05 ***
## SEMANA50 1.6692 0.3760 4.440 9.72e-06 ***
## SEMANA51 2.1104 0.3754 5.621 2.29e-08 ***
## SEMANA52 1.1986 0.3754 3.193 0.001441 **
## SEMANA53 -0.2564 0.2963 -0.865 0.386997
## Feriado_Lunes -3.4841 0.3029 -11.502 < 2e-16 ***
## Feriado_Otro -1.9867 0.2819 -7.047 2.86e-12 ***
## Madre 0.3182 0.2776 1.146 0.251983
## Semana_Santa -1.6247 0.3119 -5.208 2.19e-07 ***
## Viernes_Desp_Quincena_v2 0.5320 0.2999 1.774 0.076341 .
## Feria_Flores 0.4970 0.3951 1.258 0.208673
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 10.05 on 1395 degrees of freedom
## Multiple R-squared: 0.4236, Adjusted R-squared: 0.3967
## F-statistic: 15.77 on 65 and 1395 DF, p-value: < 2.2e-16caret_lm_fit_m## Linear Regression
##
## 1461 samples
## 9 predictor
##
## Pre-processing: centered (65), scaled (65)
## Resampling: Cross-Validated (10 fold)
## Summary of sample sizes: 1314, 1315, 1315, 1316, 1317, 1315, ...
## Resampling results:
##
## RMSE Rsquared MAE
## 10.28203 0.3716403 8.066983
##
## Tuning parameter 'intercept' was held constant at a value of TRUECalculo MSE y RMSE para los datos de entrenamiento
y_tr_pred_lm_m<-predict(caret_lm_fit_m,Train_D_Dataset[,c("Ano_Base","DIA","SEMANA","Feriado_Lunes","Feriado_Otro","Madre","Semana_Santa","Viernes_Desp_Quincena_v2","Feria_Flores")])mse_tr_lm_m<-mean((Train_D_Dataset$ACCIDENTES_GRAVES-y_tr_pred_lm_m)^2) # calcula el mse de entrenamiento
RMSE_tr_lm_m = sqrt(mse_tr_lm_m)
mse_tr_lm_m## [1] 96.34588RMSE_tr_lm_m## [1] 9.815594Calculo MSE y RMSE para los datos de validación
y_test_pred_lm_m<-predict(caret_lm_fit_m,Test_D_Dataset[,c("Ano_Base","DIA","SEMANA","Feriado_Lunes","Feriado_Otro","Madre","Semana_Santa","Viernes_Desp_Quincena_v2","Feria_Flores")])
mse_test_lm_m<-mean((Test_D_Dataset$ACCIDENTES_GRAVES-y_test_pred_lm_m)^2) # calcula el mse de entrenamiento
RMSE_test_lm_m = sqrt(mse_test_lm_m)
mse_test_lm_m## [1] 128.5295RMSE_test_lm_m## [1] 11.33709Predicción en la muestra
plot_ly (data=Train_D_Dataset,
x = ~FECHA,
y = ~ACCIDENTES_GRAVES,
type = "scatter" ,mode = "lines",
name='Real',
line=list(width=1,color='rgb(205, 12, 24)'))%>%
add_trace(y= ~y_tr_pred_lm_m,
name='Modelo lm',
line=list(width=1,color='rgb(22, 96, 167)'))%>%
layout(title='Total accidentes graves',
xaxis=list(title="Fecha"),
yaxis=list(title="Accidentes graves"),
legend = list(x = 0.75, y = 0.9))Gráfica serie 2018
plot_ly (data=Test_D_Dataset,
x = ~FECHA,
y = ~ACCIDENTES_GRAVES,
type = "scatter" ,mode = "lines",
name='Real',
line=list(width=1,color='rgb(205, 12, 24)'))%>%
add_trace(y= ~y_test_pred_lm_m,
name='Modelo lm',
line=list(width=1,color='rgb(22, 96, 167)'))%>%
layout(title='Total accidentes graves',
xaxis=list(title="Fecha"),
yaxis=list(title="Accidentes graves"),
legend = list(x = 0.75, y = 0.9))Accidentes Leves
trcntrl = trainControl(method="cv", number=10)
caret_lm_fit_sd = caret::train(ACCIDENTES_LEVES∼Ano_Base+DIA+SEMANA+Feriado_Lunes+Feriado_Otro+Madre+Semana_Santa+Viernes_Desp_Quincena_v2+Feria_Flores, data=Train_D_Dataset,
method = "lm", trControl = trcntrl,
preProcess=c("center", "scale"),
tuneLength = 10)summary(caret_lm_fit_sd)##
## Call:
## lm(formula = .outcome ~ ., data = dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -40.441 -5.720 -0.491 5.764 39.811
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 51.2669 0.2473 207.305 < 2e-16 ***
## Ano_Base 0.8573 0.2520 3.402 0.000689 ***
## DIA2 0.3115 0.3467 0.899 0.369072
## DIA3 -0.6953 0.3471 -2.003 0.045344 *
## DIA4 -0.8937 0.3486 -2.563 0.010471 *
## DIA5 1.1490 0.3671 3.130 0.001783 **
## DIA6 -2.3378 0.3476 -6.726 2.53e-11 ***
## DIA7 -10.9224 0.3494 -31.258 < 2e-16 ***
## SEMANA02 1.1493 0.3546 3.241 0.001219 **
## SEMANA03 1.8741 0.3539 5.296 1.38e-07 ***
## SEMANA04 2.2843 0.3537 6.458 1.46e-10 ***
## SEMANA05 2.2341 0.3538 6.314 3.65e-10 ***
## SEMANA06 2.3823 0.3537 6.735 2.39e-11 ***
## SEMANA07 3.1096 0.3541 8.780 < 2e-16 ***
## SEMANA08 2.4656 0.3537 6.970 4.86e-12 ***
## SEMANA09 2.5708 0.3541 7.259 6.45e-13 ***
## SEMANA10 3.0435 0.3537 8.604 < 2e-16 ***
## SEMANA11 2.7660 0.3549 7.794 1.26e-14 ***
## SEMANA12 2.6036 0.3594 7.245 7.15e-13 ***
## SEMANA13 2.6339 0.3552 7.415 2.10e-13 ***
## SEMANA14 3.0142 0.3629 8.305 2.34e-16 ***
## SEMANA15 3.2004 0.3629 8.818 < 2e-16 ***
## SEMANA16 2.2263 0.3591 6.199 7.45e-10 ***
## SEMANA17 3.2680 0.3533 9.250 < 2e-16 ***
## SEMANA18 3.2390 0.3542 9.145 < 2e-16 ***
## SEMANA19 2.8110 0.3613 7.780 1.41e-14 ***
## SEMANA20 2.9627 0.3541 8.366 < 2e-16 ***
## SEMANA21 2.7913 0.3538 7.890 6.07e-15 ***
## SEMANA22 2.4386 0.3545 6.880 9.03e-12 ***
## SEMANA23 2.7742 0.3540 7.836 9.17e-15 ***
## SEMANA24 2.6664 0.3542 7.527 9.24e-14 ***
## SEMANA25 2.3089 0.3540 6.522 9.71e-11 ***
## SEMANA26 2.0566 0.3545 5.802 8.11e-09 ***
## SEMANA27 2.4854 0.3551 6.999 4.00e-12 ***
## SEMANA28 2.5378 0.3538 7.172 1.19e-12 ***
## SEMANA29 2.9172 0.3534 8.256 3.46e-16 ***
## SEMANA30 2.8334 0.3604 7.863 7.47e-15 ***
## SEMANA31 3.0873 0.4186 7.375 2.81e-13 ***
## SEMANA32 3.1817 0.3863 8.235 4.07e-16 ***
## SEMANA33 2.9113 0.3542 8.219 4.64e-16 ***
## SEMANA34 2.4290 0.3545 6.852 1.09e-11 ***
## SEMANA35 2.3994 0.3541 6.775 1.83e-11 ***
## SEMANA36 2.7301 0.3537 7.718 2.24e-14 ***
## SEMANA37 3.0312 0.3541 8.559 < 2e-16 ***
## SEMANA38 3.1758 0.3537 8.978 < 2e-16 ***
## SEMANA39 2.3418 0.3538 6.618 5.16e-11 ***
## SEMANA40 3.2542 0.3537 9.200 < 2e-16 ***
## SEMANA41 2.4460 0.3537 6.915 7.10e-12 ***
## SEMANA42 2.7241 0.3552 7.668 3.25e-14 ***
## SEMANA43 2.7546 0.3537 7.787 1.33e-14 ***
## SEMANA44 2.8745 0.3541 8.117 1.04e-15 ***
## SEMANA45 2.4021 0.3551 6.764 1.97e-11 ***
## SEMANA46 3.1195 0.3545 8.800 < 2e-16 ***
## SEMANA47 2.9163 0.3540 8.237 4.02e-16 ***
## SEMANA48 2.9578 0.3541 8.352 < 2e-16 ***
## SEMANA49 3.3749 0.3531 9.557 < 2e-16 ***
## SEMANA50 3.4051 0.3538 9.625 < 2e-16 ***
## SEMANA51 3.7088 0.3533 10.498 < 2e-16 ***
## SEMANA52 1.6083 0.3533 4.552 5.77e-06 ***
## SEMANA53 0.3677 0.2788 1.319 0.187439
## Feriado_Lunes -5.9001 0.2851 -20.698 < 2e-16 ***
## Feriado_Otro -3.6138 0.2653 -13.622 < 2e-16 ***
## Madre 0.5225 0.2613 2.000 0.045686 *
## Semana_Santa -1.3848 0.2935 -4.718 2.62e-06 ***
## Viernes_Desp_Quincena_v2 0.2077 0.2822 0.736 0.461970
## Feria_Flores 0.8274 0.3718 2.225 0.026224 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.453 on 1395 degrees of freedom
## Multiple R-squared: 0.6824, Adjusted R-squared: 0.6676
## F-statistic: 46.11 on 65 and 1395 DF, p-value: < 2.2e-16caret_lm_fit_sd## Linear Regression
##
## 1461 samples
## 9 predictor
##
## Pre-processing: centered (65), scaled (65)
## Resampling: Cross-Validated (10 fold)
## Summary of sample sizes: 1315, 1315, 1316, 1315, 1315, 1316, ...
## Resampling results:
##
## RMSE Rsquared MAE
## 9.740971 0.649214 7.557774
##
## Tuning parameter 'intercept' was held constant at a value of TRUECalculo MSE y RMSE para los datos de entrenamiento
y_tr_pred_lm_sd<-predict(caret_lm_fit_sd,Train_D_Dataset[,c("Ano_Base","DIA","SEMANA","Feriado_Lunes","Feriado_Otro","Madre","Semana_Santa","Viernes_Desp_Quincena_v2","Feria_Flores")])mse_tr_lm_sd<-mean((Train_D_Dataset$ACCIDENTES_LEVES-y_tr_pred_lm_sd)^2) # calcula el mse de entrenamiento
RMSE_tr_lm_sd = sqrt(mse_tr_lm_sd)
mse_tr_lm_sd## [1] 85.31586RMSE_tr_lm_sd## [1] 9.236658Calculo MSE y RMSE para los datos de validación
y_test_pred_lm_sd<-predict(caret_lm_fit_sd,Test_D_Dataset[,c("Ano_Base","DIA","SEMANA","Feriado_Lunes","Feriado_Otro","Madre","Semana_Santa","Viernes_Desp_Quincena_v2","Feria_Flores")])
mse_test_lm_sd<-mean((Test_D_Dataset$ACCIDENTES_LEVES-y_test_pred_lm_sd)^2) # calcula el mse de entrenamiento
RMSE_test_lm_sd = sqrt(mse_test_lm_sd)
mse_test_lm_sd## [1] 108.411RMSE_test_lm_sd## [1] 10.41206Predicción en la muestra
plot_ly (data=Train_D_Dataset,
x = ~FECHA,
y = ~ACCIDENTES_LEVES,
type = "scatter" ,mode = "lines",
name='Real',
line=list(width=1,color='rgb(205, 12, 24)'))%>%
add_trace(y= ~y_tr_pred_lm_sd,
name='Modelo lm',
line=list(width=1,color='rgb(22, 96, 167)'))%>%
layout(title='Total accidentes leves',
xaxis=list(title="Fecha"),
yaxis=list(title="Accidentes leves"),
legend = list(x = 0.75, y = 0.9))Gráfica serie 2018
plot_ly (data=Test_D_Dataset,
x = ~FECHA,
y = ~ACCIDENTES_LEVES,
type = "scatter" ,mode = "lines",
name='Real',
line=list(width=1,color='rgb(205, 12, 24)'))%>%
add_trace(y= ~y_test_pred_lm_sd,
name='Modelo lm',
line=list(width=1,color='rgb(22, 96, 167)'))%>%
layout(title='Total accidentes leves',
xaxis=list(title="Fecha"),
yaxis=list(title="Accidentes leves"),
legend = list(x = 0.75, y = 0.9))Resumen Modelos Regresión lineal para los diferentes tipos de accidente
Tipo_de_accidentes= c("Total Accidentes","Accidentes graves","Accidentes leves")
RMSE_Train_lm = round(c(RMSE_tr_lm,RMSE_tr_lm_m,RMSE_tr_lm_sd), 3)
RMSE_Test_lm = round(c(RMSE_test_lm,RMSE_test_lm_m,RMSE_test_lm_sd),3)
Tabla_lm = data.frame (cbind(Tipo_de_accidentes,RMSE_Train_lm,RMSE_Test_lm))
Tabla_lm## Tipo_de_accidentes RMSE_Train_lm RMSE_Test_lm
## 1 Total Accidentes 14.701 16.026
## 2 Accidentes graves 9.816 11.337
## 3 Accidentes leves 9.237 10.4122. KNN
head(Train_D_Dataset)## FECHA ACCIDENTES_GRAVES ACCIDENTES_LEVES TOTAL_ACCIDENTES Ano_Base
## 1 2014-01-01 56 18 74 0
## 2 2014-01-02 42 30 72 0
## 3 2014-01-03 51 42 93 0
## 4 2014-01-04 41 27 68 0
## 5 2014-01-05 36 31 67 0
## 6 2014-01-06 29 14 43 0
## Lunes martes miercoles jueves viernes sabado domingo Enero Febrero Marzo
## 1 0 0 1 0 0 0 0 1 0 0
## 2 0 0 0 1 0 0 0 1 0 0
## 3 0 0 0 0 1 0 0 1 0 0
## 4 0 0 0 0 0 1 0 1 0 0
## 5 0 0 0 0 0 0 1 1 0 0
## 6 1 0 0 0 0 0 0 1 0 0
## Abril Mayo Junio Julio Agosto Septiembre Octubre Noviembre Diciembre
## 1 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0
## Feriado Feriado_v1 Feriado_Lunes Feriado_Otro Previo_feriado
## 1 1 1 0 1 0
## 2 0 0 0 0 0
## 3 0 0 0 0 1
## 4 0 0 0 0 1
## 5 0 0 0 0 1
## 6 1 1 1 0 0
## Semana_Santa Semana_Santa_Mes Semana_Santa_Semana Prima Mujer Padre
## 1 0 0 0 0 0 0
## 2 0 0 0 0 0 0
## 3 0 0 0 0 0 0
## 4 0 0 0 0 0 0
## 5 0 0 0 0 0 0
## 6 0 0 0 0 0 0
## Madre AmoryAmistad Semana_Santa_v1 Viernes_Antes_Puente Quincena
## 1 0 0 0 0 0
## 2 0 0 0 0 0
## 3 0 0 0 1 0
## 4 0 0 0 0 0
## 5 0 0 0 0 0
## 6 0 0 0 0 0
## Viernes_Desp_Quincena Viernes_Desp_Quincena_v1 Viernes_Desp_Quincena_v2
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Feria_Flores Feria_Flores_Mes Feria_Flores_Semana ANO SEMANA MES DIA
## 1 0 0 0 2014 01 01 3
## 2 0 0 0 2014 01 01 4
## 3 0 0 0 2014 01 01 5
## 4 0 0 0 2014 01 01 6
## 5 0 0 0 2014 01 01 7
## 6 0 0 0 2014 02 01 1Total Accidentes
library(caret)trcntrl = trainControl(method="cv", number=10)
caret_knn_fit = caret::train(TOTAL_ACCIDENTES∼Ano_Base+DIA+SEMANA+Feriado_Lunes+Feriado_Otro+Madre+Semana_Santa+Viernes_Desp_Quincena_v2+Feria_Flores, data=Train_D_Dataset,
method = "knn", trControl = trcntrl,
preProcess=c("center", "scale"),
tuneLength = 10)summary(caret_knn_fit)## Length Class Mode
## learn 2 -none- list
## k 1 -none- numeric
## theDots 0 -none- list
## xNames 65 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 1 -none- logical
## param 0 -none- listcaret_knn_fit## k-Nearest Neighbors
##
## 1461 samples
## 9 predictor
##
## Pre-processing: centered (65), scaled (65)
## Resampling: Cross-Validated (10 fold)
## Summary of sample sizes: 1315, 1315, 1315, 1316, 1315, 1315, ...
## Resampling results across tuning parameters:
##
## k RMSE Rsquared MAE
## 5 21.10115 0.3824778 16.62695
## 7 22.19154 0.3152980 17.49614
## 9 23.03506 0.2534354 18.00843
## 11 23.49203 0.2133221 18.25785
## 13 23.77937 0.1872929 18.44687
## 15 24.07922 0.1625583 18.69741
## 17 24.48434 0.1315898 18.96446
## 19 24.62593 0.1187803 19.10631
## 21 24.83848 0.1020593 19.37665
## 23 24.76398 0.1031986 19.49218
##
## RMSE was used to select the optimal model using the smallest value.
## The final value used for the model was k = 5.Calculo MSE y RMSE para los datos de entrenamiento
y_tr_pred_knn<-predict(caret_knn_fit,Train_D_Dataset[,c("Ano_Base","DIA","SEMANA","Feriado_Lunes","Feriado_Otro","Madre","Semana_Santa","Viernes_Desp_Quincena_v2","Feria_Flores")])
mse_tr_knn<-mean((Train_D_Dataset$TOTAL_ACCIDENTES-y_tr_pred_knn)^2) # calcula el mse de entrenamiento
RMSE_tr_knn = sqrt(mse_tr_knn)
mse_tr_knn## [1] 305.9636RMSE_tr_knn## [1] 17.49181Calculo MSE y RMSE para los datos de validación
y_test_pred_knn<-predict(caret_knn_fit,Test_D_Dataset[,c("Ano_Base","DIA","SEMANA","Feriado_Lunes","Feriado_Otro","Madre","Semana_Santa","Viernes_Desp_Quincena_v2","Feria_Flores")])
mse_test_knn<-mean((Test_D_Dataset$TOTAL_ACCIDENTES-y_test_pred_knn)^2) # calcula el mse de entrenamiento
RMSE_test_knn = sqrt(mse_test_knn)
mse_test_knn## [1] 437.2166RMSE_test_knn## [1] 20.90972Predicción en la muestra
plot_ly (data=Train_D_Dataset,
x = ~FECHA,
y = ~TOTAL_ACCIDENTES,
type = "scatter" ,mode = "lines",
name='Real',
line=list(width=1,color='rgb(205, 12, 24)'))%>%
add_trace(y= ~y_tr_pred_knn,
name='Modelo knn',
line=list(width=1,color='rgb(22, 96, 167)'))%>%
layout(title='Total Accidentes',
xaxis=list(title="Fecha"),
yaxis=list(title="Accidentes"),
legend = list(x = 0.75, y = 0.9))plot_ly (data=Test_D_Dataset,
x = ~FECHA,
y = ~TOTAL_ACCIDENTES,
type = "scatter" ,mode = "lines",
name='Real',
line=list(width=1,color='rgb(205, 12, 24)'))%>%
add_trace(y= ~y_test_pred_knn,
name='Modelo knn',
line=list(width=1,color='rgb(22, 96, 167)'))%>%
layout(title='Total Accidentes',
xaxis=list(title="Fecha"),
yaxis=list(title="Accidentes"),
legend = list(x = 0.75, y = 0.9))Accidentes graves
trcntrl = trainControl(method="cv", number=10)
caret_knn_fit_m = caret::train(ACCIDENTES_GRAVES∼Ano_Base+DIA+SEMANA+Feriado_Lunes+Feriado_Otro+Madre+Semana_Santa+Viernes_Desp_Quincena_v2+Feria_Flores, data=Train_D_Dataset,
method = "knn", trControl = trcntrl,
preProcess=c("center", "scale"),
tuneLength = 10)summary(caret_knn_fit_m)## Length Class Mode
## learn 2 -none- list
## k 1 -none- numeric
## theDots 0 -none- list
## xNames 65 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 1 -none- logical
## param 0 -none- listcaret_knn_fit_m## k-Nearest Neighbors
##
## 1461 samples
## 9 predictor
##
## Pre-processing: centered (65), scaled (65)
## Resampling: Cross-Validated (10 fold)
## Summary of sample sizes: 1314, 1314, 1315, 1315, 1316, 1315, ...
## Resampling results across tuning parameters:
##
## k RMSE Rsquared MAE
## 5 11.63945 0.21901439 9.233752
## 7 11.81686 0.18953717 9.335533
## 9 11.96100 0.16399084 9.379428
## 11 11.96903 0.15666172 9.363416
## 13 12.02683 0.14625821 9.366623
## 15 12.10142 0.13512843 9.399686
## 17 12.17010 0.12553370 9.470361
## 19 12.24001 0.11374812 9.511199
## 21 12.28834 0.10543240 9.571437
## 23 12.31755 0.09927642 9.615764
##
## RMSE was used to select the optimal model using the smallest value.
## The final value used for the model was k = 5.Calculo MSE y RMSE para los datos de entrenamiento
y_tr_pred_knn_m<-predict(caret_knn_fit_m,Train_D_Dataset[,c("Ano_Base","DIA","SEMANA","Feriado_Lunes","Feriado_Otro","Madre","Semana_Santa","Viernes_Desp_Quincena_v2","Feria_Flores")])
mse_tr_knn_m<-mean((Train_D_Dataset$ACCIDENTES_GRAVES-y_tr_pred_knn_m)^2) # calcula el mse de entrenamiento
RMSE_tr_knn_m = sqrt(mse_tr_knn_m)
mse_tr_knn_m## [1] 96.31957RMSE_tr_knn_m## [1] 9.814253Calculo MSE y RMSE para los datos de validación
y_test_pred_knn_m<-predict(caret_knn_fit_m,Test_D_Dataset[,c("Ano_Base","DIA","SEMANA","Feriado_Lunes","Feriado_Otro","Madre","Semana_Santa","Viernes_Desp_Quincena_v2","Feria_Flores")])
mse_test_knn_m<-mean((Test_D_Dataset$ACCIDENTES_GRAVES-y_test_pred_knn_m)^2) # calcula el mse de entrenamiento
RMSE_test_knn_m = sqrt(mse_test_knn_m)
mse_test_knn_m## [1] 179.9912RMSE_test_knn_m## [1] 13.41608Predicción en la muestra
plot_ly (data=Train_D_Dataset,
x = ~FECHA,
y = ~ACCIDENTES_GRAVES,
type = "scatter" ,mode = "lines",
name='Real',
line=list(width=1,color='rgb(205, 12, 24)'))%>%
add_trace(y= ~y_tr_pred_knn_m,
name='Modelo knn',
line=list(width=1,color='rgb(22, 96, 167)'))%>%
layout(title='Total accidentes graves',
xaxis=list(title="Fecha"),
yaxis=list(title="Accidentes graves"),
legend = list(x = 0.75, y = 0.9))plot_ly (data=Test_D_Dataset,
x = ~FECHA,
y = ~ACCIDENTES_GRAVES,
type = "scatter" ,mode = "lines",
name='Real',
line=list(width=1,color='rgb(205, 12, 24)'))%>%
add_trace(y= ~y_test_pred_knn_m,
name='Modelo knn',
line=list(width=1,color='rgb(22, 96, 167)'))%>%
layout(title='Total accidentes graves',
xaxis=list(title="Fecha"),
yaxis=list(title="Accidentes graves"),
legend = list(x = 0.75, y = 0.9))Accidentes Leves
trcntrl = trainControl(method="cv", number=10)
caret_knn_fit_sd = caret::train(ACCIDENTES_LEVES∼Ano_Base+DIA+SEMANA+Feriado_Lunes+Feriado_Otro+Madre+Semana_Santa+Viernes_Desp_Quincena_v2+Feria_Flores, data=Train_D_Dataset,
method = "knn", trControl = trcntrl,
preProcess=c("center", "scale"),
tuneLength = 10)summary(caret_knn_fit_sd)## Length Class Mode
## learn 2 -none- list
## k 1 -none- numeric
## theDots 0 -none- list
## xNames 65 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 1 -none- logical
## param 0 -none- listcaret_knn_fit_sd## k-Nearest Neighbors
##
## 1461 samples
## 9 predictor
##
## Pre-processing: centered (65), scaled (65)
## Resampling: Cross-Validated (10 fold)
## Summary of sample sizes: 1316, 1313, 1315, 1316, 1316, 1314, ...
## Resampling results across tuning parameters:
##
## k RMSE Rsquared MAE
## 5 13.46482 0.36078923 10.78935
## 7 14.18933 0.28497651 11.34931
## 9 14.74803 0.21045008 11.73284
## 11 15.06144 0.16669188 11.93018
## 13 15.19132 0.15219139 11.98480
## 15 15.37074 0.13197526 12.08169
## 17 15.57846 0.10567551 12.20996
## 19 15.71045 0.08705196 12.36730
## 21 15.82078 0.07375238 12.50320
## 23 15.80293 0.07362706 12.57585
##
## RMSE was used to select the optimal model using the smallest value.
## The final value used for the model was k = 5.Calculo MSE y RMSE para los datos de entrenamiento
y_tr_pred_knn_sd<-predict(caret_knn_fit_sd,Train_D_Dataset[,c("Ano_Base","DIA","SEMANA","Feriado_Lunes","Feriado_Otro","Madre","Semana_Santa","Viernes_Desp_Quincena_v2","Feria_Flores")])
mse_tr_knn_sd<-mean((Train_D_Dataset$ACCIDENTES_LEVES-y_tr_pred_knn_sd)^2) # calcula el mse de entrenamiento
RMSE_tr_knn_sd = sqrt(mse_tr_knn_sd)
mse_tr_knn_sd## [1] 124.3761RMSE_tr_knn_sd## [1] 11.1524Calculo MSE y RMSE para los datos de validación
y_test_pred_knn_sd<-predict(caret_knn_fit_sd,Test_D_Dataset[,c("Ano_Base","DIA","SEMANA","Feriado_Lunes","Feriado_Otro","Madre","Semana_Santa","Viernes_Desp_Quincena_v2","Feria_Flores")])
mse_test_knn_sd<-mean((Test_D_Dataset$ACCIDENTES_LEVES-y_test_pred_knn_sd)^2) # calcula el mse de entrenamiento
RMSE_test_knn_sd = sqrt(mse_test_knn_sd)
mse_test_knn_sd## [1] 158.0779RMSE_test_knn_sd## [1] 12.5729Predicción en la muestra
plot_ly (data=Train_D_Dataset,
x = ~FECHA,
y = ~ACCIDENTES_LEVES,
type = "scatter" ,mode = "lines",
name='Real',
line=list(width=1,color='rgb(205, 12, 24)'))%>%
add_trace(y= ~y_tr_pred_knn_sd,
name='Modelo knn',
line=list(width=1,color='rgb(22, 96, 167)'))%>%
layout(title='Total accidentes leves',
xaxis=list(title="Fecha"),
yaxis=list(title="Accidentes"),
legend = list(x = 0.75, y = 0.9))Gráfica serie 2018
plot_ly (data=Test_D_Dataset,
x = ~FECHA,
y = ~ACCIDENTES_LEVES,
type = "scatter" ,mode = "lines",
name='Real',
line=list(width=1,color='rgb(205, 12, 24)'))%>%
add_trace(y= ~y_test_pred_knn_sd,
name='Modelo knn',
line=list(width=1,color='rgb(22, 96, 167)'))%>%
layout(title='Total accidentes leves',
xaxis=list(title="Fecha"),
yaxis=list(title="Accidentes"),
legend = list(x = 0.75, y = 0.9))Resumen Modelos KNN para los diferentes tipos de accidente
Tipo_de_accidentes= c("Total Accidentes","Accidentes graves","Accidentes leves")
RMSE_Train_knn = round(c(RMSE_tr_knn,RMSE_tr_knn_m,RMSE_tr_knn_sd), 3)
RMSE_Test_knn = round(c(RMSE_test_knn,RMSE_test_knn_m,RMSE_test_knn_sd),3)
Tabla_knn = data.frame (cbind(Tipo_de_accidentes,RMSE_Train_knn,RMSE_Test_knn))
Tabla_knn## Tipo_de_accidentes RMSE_Train_knn RMSE_Test_knn
## 1 Total Accidentes 17.492 20.91
## 2 Accidentes graves 9.814 13.416
## 3 Accidentes leves 11.152 12.5733. MODELO LINEAL GENERALIZADO
Total Accidentes
glm_fit<-glm(TOTAL_ACCIDENTES∼Ano_Base+DIA+SEMANA+Feriado_Lunes+Feriado_Otro+Madre+Semana_Santa+Viernes_Desp_Quincena_v2+Feria_Flores, data=Train_D_Dataset, family = "poisson")
summary(glm_fit)##
## Call:
## glm(formula = TOTAL_ACCIDENTES ~ Ano_Base + DIA + SEMANA + Feriado_Lunes +
## Feriado_Otro + Madre + Semana_Santa + Viernes_Desp_Quincena_v2 +
## Feria_Flores, family = "poisson", data = Train_D_Dataset)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -5.7817 -0.9211 -0.0185 0.7671 4.7852
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 4.488686 0.023017 195.020 < 2e-16 ***
## Ano_Base 0.018122 0.004971 3.646 0.000266 ***
## DIA2 -0.002114 0.009296 -0.227 0.820100
## DIA3 -0.025356 0.009341 -2.715 0.006637 **
## DIA4 -0.025304 0.009398 -2.692 0.007093 **
## DIA5 0.018082 0.009815 1.842 0.065453 .
## DIA6 -0.086525 0.009484 -9.124 < 2e-16 ***
## DIA7 -0.490167 0.010603 -46.229 < 2e-16 ***
## SEMANA02 0.110017 0.029771 3.695 0.000219 ***
## SEMANA03 0.272105 0.028496 9.549 < 2e-16 ***
## SEMANA04 0.277454 0.028363 9.782 < 2e-16 ***
## SEMANA05 0.317292 0.028142 11.275 < 2e-16 ***
## SEMANA06 0.359992 0.027906 12.900 < 2e-16 ***
## SEMANA07 0.378320 0.027830 13.594 < 2e-16 ***
## SEMANA08 0.355197 0.027932 12.717 < 2e-16 ***
## SEMANA09 0.346486 0.027999 12.375 < 2e-16 ***
## SEMANA10 0.410986 0.027639 14.870 < 2e-16 ***
## SEMANA11 0.395551 0.027823 14.217 < 2e-16 ***
## SEMANA12 0.351672 0.028793 12.214 < 2e-16 ***
## SEMANA13 0.368182 0.028165 13.072 < 2e-16 ***
## SEMANA14 0.402735 0.028557 14.103 < 2e-16 ***
## SEMANA15 0.394495 0.028621 13.783 < 2e-16 ***
## SEMANA16 0.315453 0.028799 10.954 < 2e-16 ***
## SEMANA17 0.404038 0.027706 14.583 < 2e-16 ***
## SEMANA18 0.396121 0.027955 14.170 < 2e-16 ***
## SEMANA19 0.368230 0.028245 13.037 < 2e-16 ***
## SEMANA20 0.380075 0.027820 13.662 < 2e-16 ***
## SEMANA21 0.373558 0.027926 13.377 < 2e-16 ***
## SEMANA22 0.332104 0.028286 11.741 < 2e-16 ***
## SEMANA23 0.392822 0.027929 14.065 < 2e-16 ***
## SEMANA24 0.359672 0.028021 12.836 < 2e-16 ***
## SEMANA25 0.308529 0.028396 10.865 < 2e-16 ***
## SEMANA26 0.246334 0.028781 8.559 < 2e-16 ***
## SEMANA27 0.337212 0.028489 11.837 < 2e-16 ***
## SEMANA28 0.349607 0.027964 12.502 < 2e-16 ***
## SEMANA29 0.380267 0.027975 13.593 < 2e-16 ***
## SEMANA30 0.375028 0.028227 13.286 < 2e-16 ***
## SEMANA31 0.411354 0.031438 13.085 < 2e-16 ***
## SEMANA32 0.407898 0.029761 13.706 < 2e-16 ***
## SEMANA33 0.393692 0.027843 14.140 < 2e-16 ***
## SEMANA34 0.350120 0.028280 12.381 < 2e-16 ***
## SEMANA35 0.383868 0.027800 13.808 < 2e-16 ***
## SEMANA36 0.359693 0.027908 12.889 < 2e-16 ***
## SEMANA37 0.415116 0.027639 15.019 < 2e-16 ***
## SEMANA38 0.417499 0.027605 15.124 < 2e-16 ***
## SEMANA39 0.343436 0.027998 12.266 < 2e-16 ***
## SEMANA40 0.412122 0.027633 14.914 < 2e-16 ***
## SEMANA41 0.291319 0.028284 10.300 < 2e-16 ***
## SEMANA42 0.366934 0.028322 12.956 < 2e-16 ***
## SEMANA43 0.361485 0.027898 12.957 < 2e-16 ***
## SEMANA44 0.350107 0.027980 12.513 < 2e-16 ***
## SEMANA45 0.314993 0.028618 11.007 < 2e-16 ***
## SEMANA46 0.366429 0.028096 13.042 < 2e-16 ***
## SEMANA47 0.354673 0.028134 12.607 < 2e-16 ***
## SEMANA48 0.344368 0.028011 12.294 < 2e-16 ***
## SEMANA49 0.371731 0.027972 13.289 < 2e-16 ***
## SEMANA50 0.377630 0.027981 13.496 < 2e-16 ***
## SEMANA51 0.415801 0.027645 15.041 < 2e-16 ***
## SEMANA52 0.220503 0.029013 7.600 2.96e-14 ***
## SEMANA53 0.023245 0.047561 0.489 0.625025
## Feriado_Lunes -0.576941 0.019813 -29.119 < 2e-16 ***
## Feriado_Otro -0.459388 0.024470 -18.774 < 2e-16 ***
## Madre 0.196891 0.053169 3.703 0.000213 ***
## Semana_Santa -0.188386 0.020682 -9.109 < 2e-16 ***
## Viernes_Desp_Quincena_v2 0.029713 0.015773 1.884 0.059595 .
## Feria_Flores 0.070515 0.021489 3.281 0.001033 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 9170.5 on 1460 degrees of freedom
## Residual deviance: 2710.5 on 1395 degrees of freedom
## AIC: 12428
##
## Number of Fisher Scoring iterations: 4glm_fit##
## Call: glm(formula = TOTAL_ACCIDENTES ~ Ano_Base + DIA + SEMANA + Feriado_Lunes +
## Feriado_Otro + Madre + Semana_Santa + Viernes_Desp_Quincena_v2 +
## Feria_Flores, family = "poisson", data = Train_D_Dataset)
##
## Coefficients:
## (Intercept) Ano_Base
## 4.488686 0.018122
## DIA2 DIA3
## -0.002114 -0.025356
## DIA4 DIA5
## -0.025304 0.018082
## DIA6 DIA7
## -0.086525 -0.490167
## SEMANA02 SEMANA03
## 0.110017 0.272105
## SEMANA04 SEMANA05
## 0.277454 0.317292
## SEMANA06 SEMANA07
## 0.359992 0.378320
## SEMANA08 SEMANA09
## 0.355197 0.346486
## SEMANA10 SEMANA11
## 0.410986 0.395551
## SEMANA12 SEMANA13
## 0.351672 0.368182
## SEMANA14 SEMANA15
## 0.402735 0.394495
## SEMANA16 SEMANA17
## 0.315453 0.404038
## SEMANA18 SEMANA19
## 0.396121 0.368230
## SEMANA20 SEMANA21
## 0.380075 0.373558
## SEMANA22 SEMANA23
## 0.332104 0.392822
## SEMANA24 SEMANA25
## 0.359672 0.308529
## SEMANA26 SEMANA27
## 0.246334 0.337212
## SEMANA28 SEMANA29
## 0.349607 0.380267
## SEMANA30 SEMANA31
## 0.375028 0.411354
## SEMANA32 SEMANA33
## 0.407898 0.393692
## SEMANA34 SEMANA35
## 0.350120 0.383868
## SEMANA36 SEMANA37
## 0.359693 0.415116
## SEMANA38 SEMANA39
## 0.417499 0.343436
## SEMANA40 SEMANA41
## 0.412122 0.291319
## SEMANA42 SEMANA43
## 0.366934 0.361485
## SEMANA44 SEMANA45
## 0.350107 0.314993
## SEMANA46 SEMANA47
## 0.366429 0.354673
## SEMANA48 SEMANA49
## 0.344368 0.371731
## SEMANA50 SEMANA51
## 0.377630 0.415801
## SEMANA52 SEMANA53
## 0.220503 0.023245
## Feriado_Lunes Feriado_Otro
## -0.576941 -0.459388
## Madre Semana_Santa
## 0.196891 -0.188386
## Viernes_Desp_Quincena_v2 Feria_Flores
## 0.029713 0.070515
##
## Degrees of Freedom: 1460 Total (i.e. Null); 1395 Residual
## Null Deviance: 9171
## Residual Deviance: 2710 AIC: 12430Calculo MSE y RMSE para los datos de entrenamiento
y_tr_pred_glm<-predict(glm_fit,Train_D_Dataset[,c("Ano_Base","DIA","SEMANA","Feriado_Lunes","Feriado_Otro","Madre","Semana_Santa","Viernes_Desp_Quincena_v2","Feria_Flores")],type="response")
mse_tr_glm<-mean((Train_D_Dataset$TOTAL_ACCIDENTES-y_tr_pred_glm)^2) # calcula el mse de entrenamiento
RMSE_tr_glm = sqrt(mse_tr_glm)
mse_tr_glm## [1] 210.0413RMSE_tr_glm## [1] 14.4928Calculo MSE y RMSE para los datos de validación
y_test_pred_glm<-predict(glm_fit,Test_D_Dataset[,c("Ano_Base","DIA","SEMANA","Feriado_Lunes","Feriado_Otro","Madre","Semana_Santa","Viernes_Desp_Quincena_v2","Feria_Flores")],type="response")
mse_test_glm<-mean((Train_D_Dataset$TOTAL_ACCIDENTES-y_test_pred_glm)^2) # calcula el mse de entrenamiento## Warning in Train_D_Dataset$TOTAL_ACCIDENTES - y_test_pred_glm: longitud de
## objeto mayor no es múltiplo de la longitud de uno menorRMSE_test_glm = sqrt(mse_test_glm)
mse_test_glm## [1] 1126.705RMSE_test_glm## [1] 33.56643Predicción en la muestra
plot_ly (data=Train_D_Dataset,
x = ~FECHA,
y = ~TOTAL_ACCIDENTES,
type = "scatter" ,mode = "lines",
name='Real',
line=list(width=1,color='rgb(205, 12, 24)'))%>%
add_trace(y= ~y_tr_pred_glm,
name='Modelo glm',
line=list(width=1,color='rgb(22, 96, 167)'))%>%
layout(title='Total accidentes',
xaxis=list(title="Fecha"),
yaxis=list(title="Accidentes"),
legend = list(x = 0.75, y = 0.9))Gráfica serie 2018
plot_ly (data=Test_D_Dataset,
x = ~FECHA,
y = ~TOTAL_ACCIDENTES,
type = "scatter" ,mode = "lines",
name='Real',
line=list(width=1,color='rgb(205, 12, 24)'))%>%
add_trace(y= ~y_test_pred_glm,
name='Modelo glm',
line=list(width=1,color='rgb(22, 96, 167)'))%>%
layout(title='Total accidentes',
xaxis=list(title="Fecha"),
yaxis=list(title="Accidentes"),
legend = list(x = 0.75, y = 0.9))Accidentes Graves
glm_fit_m<-glm(ACCIDENTES_GRAVES∼Ano_Base+DIA+SEMANA+Feriado_Lunes+Feriado_Otro+Madre+Semana_Santa+Viernes_Desp_Quincena_v2+Feria_Flores, data=Train_D_Dataset, family = "poisson")
summary(glm_fit)##
## Call:
## glm(formula = TOTAL_ACCIDENTES ~ Ano_Base + DIA + SEMANA + Feriado_Lunes +
## Feriado_Otro + Madre + Semana_Santa + Viernes_Desp_Quincena_v2 +
## Feria_Flores, family = "poisson", data = Train_D_Dataset)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -5.7817 -0.9211 -0.0185 0.7671 4.7852
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 4.488686 0.023017 195.020 < 2e-16 ***
## Ano_Base 0.018122 0.004971 3.646 0.000266 ***
## DIA2 -0.002114 0.009296 -0.227 0.820100
## DIA3 -0.025356 0.009341 -2.715 0.006637 **
## DIA4 -0.025304 0.009398 -2.692 0.007093 **
## DIA5 0.018082 0.009815 1.842 0.065453 .
## DIA6 -0.086525 0.009484 -9.124 < 2e-16 ***
## DIA7 -0.490167 0.010603 -46.229 < 2e-16 ***
## SEMANA02 0.110017 0.029771 3.695 0.000219 ***
## SEMANA03 0.272105 0.028496 9.549 < 2e-16 ***
## SEMANA04 0.277454 0.028363 9.782 < 2e-16 ***
## SEMANA05 0.317292 0.028142 11.275 < 2e-16 ***
## SEMANA06 0.359992 0.027906 12.900 < 2e-16 ***
## SEMANA07 0.378320 0.027830 13.594 < 2e-16 ***
## SEMANA08 0.355197 0.027932 12.717 < 2e-16 ***
## SEMANA09 0.346486 0.027999 12.375 < 2e-16 ***
## SEMANA10 0.410986 0.027639 14.870 < 2e-16 ***
## SEMANA11 0.395551 0.027823 14.217 < 2e-16 ***
## SEMANA12 0.351672 0.028793 12.214 < 2e-16 ***
## SEMANA13 0.368182 0.028165 13.072 < 2e-16 ***
## SEMANA14 0.402735 0.028557 14.103 < 2e-16 ***
## SEMANA15 0.394495 0.028621 13.783 < 2e-16 ***
## SEMANA16 0.315453 0.028799 10.954 < 2e-16 ***
## SEMANA17 0.404038 0.027706 14.583 < 2e-16 ***
## SEMANA18 0.396121 0.027955 14.170 < 2e-16 ***
## SEMANA19 0.368230 0.028245 13.037 < 2e-16 ***
## SEMANA20 0.380075 0.027820 13.662 < 2e-16 ***
## SEMANA21 0.373558 0.027926 13.377 < 2e-16 ***
## SEMANA22 0.332104 0.028286 11.741 < 2e-16 ***
## SEMANA23 0.392822 0.027929 14.065 < 2e-16 ***
## SEMANA24 0.359672 0.028021 12.836 < 2e-16 ***
## SEMANA25 0.308529 0.028396 10.865 < 2e-16 ***
## SEMANA26 0.246334 0.028781 8.559 < 2e-16 ***
## SEMANA27 0.337212 0.028489 11.837 < 2e-16 ***
## SEMANA28 0.349607 0.027964 12.502 < 2e-16 ***
## SEMANA29 0.380267 0.027975 13.593 < 2e-16 ***
## SEMANA30 0.375028 0.028227 13.286 < 2e-16 ***
## SEMANA31 0.411354 0.031438 13.085 < 2e-16 ***
## SEMANA32 0.407898 0.029761 13.706 < 2e-16 ***
## SEMANA33 0.393692 0.027843 14.140 < 2e-16 ***
## SEMANA34 0.350120 0.028280 12.381 < 2e-16 ***
## SEMANA35 0.383868 0.027800 13.808 < 2e-16 ***
## SEMANA36 0.359693 0.027908 12.889 < 2e-16 ***
## SEMANA37 0.415116 0.027639 15.019 < 2e-16 ***
## SEMANA38 0.417499 0.027605 15.124 < 2e-16 ***
## SEMANA39 0.343436 0.027998 12.266 < 2e-16 ***
## SEMANA40 0.412122 0.027633 14.914 < 2e-16 ***
## SEMANA41 0.291319 0.028284 10.300 < 2e-16 ***
## SEMANA42 0.366934 0.028322 12.956 < 2e-16 ***
## SEMANA43 0.361485 0.027898 12.957 < 2e-16 ***
## SEMANA44 0.350107 0.027980 12.513 < 2e-16 ***
## SEMANA45 0.314993 0.028618 11.007 < 2e-16 ***
## SEMANA46 0.366429 0.028096 13.042 < 2e-16 ***
## SEMANA47 0.354673 0.028134 12.607 < 2e-16 ***
## SEMANA48 0.344368 0.028011 12.294 < 2e-16 ***
## SEMANA49 0.371731 0.027972 13.289 < 2e-16 ***
## SEMANA50 0.377630 0.027981 13.496 < 2e-16 ***
## SEMANA51 0.415801 0.027645 15.041 < 2e-16 ***
## SEMANA52 0.220503 0.029013 7.600 2.96e-14 ***
## SEMANA53 0.023245 0.047561 0.489 0.625025
## Feriado_Lunes -0.576941 0.019813 -29.119 < 2e-16 ***
## Feriado_Otro -0.459388 0.024470 -18.774 < 2e-16 ***
## Madre 0.196891 0.053169 3.703 0.000213 ***
## Semana_Santa -0.188386 0.020682 -9.109 < 2e-16 ***
## Viernes_Desp_Quincena_v2 0.029713 0.015773 1.884 0.059595 .
## Feria_Flores 0.070515 0.021489 3.281 0.001033 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 9170.5 on 1460 degrees of freedom
## Residual deviance: 2710.5 on 1395 degrees of freedom
## AIC: 12428
##
## Number of Fisher Scoring iterations: 4glm_fit_m##
## Call: glm(formula = ACCIDENTES_GRAVES ~ Ano_Base + DIA + SEMANA + Feriado_Lunes +
## Feriado_Otro + Madre + Semana_Santa + Viernes_Desp_Quincena_v2 +
## Feria_Flores, family = "poisson", data = Train_D_Dataset)
##
## Coefficients:
## (Intercept) Ano_Base
## 3.976112 0.006397
## DIA2 DIA3
## -0.017911 -0.018039
## DIA4 DIA5
## -0.010024 -0.016975
## DIA6 DIA7
## -0.058562 -0.299248
## SEMANA02 SEMANA03
## 0.025966 0.209462
## SEMANA04 SEMANA05
## 0.168745 0.246114
## SEMANA06 SEMANA07
## 0.305105 0.258630
## SEMANA08 SEMANA09
## 0.287786 0.260259
## SEMANA10 SEMANA11
## 0.325703 0.328136
## SEMANA12 SEMANA13
## 0.267391 0.293593
## SEMANA14 SEMANA15
## 0.312125 0.274098
## SEMANA16 SEMANA17
## 0.247631 0.293937
## SEMANA18 SEMANA19
## 0.270797 0.273891
## SEMANA20 SEMANA21
## 0.277996 0.284721
## SEMANA22 SEMANA23
## 0.248920 0.319613
## SEMANA24 SEMANA25
## 0.273177 0.223305
## SEMANA26 SEMANA27
## 0.142363 0.253616
## SEMANA28 SEMANA29
## 0.269599 0.284379
## SEMANA30 SEMANA31
## 0.282785 0.330713
## SEMANA32 SEMANA33
## 0.300057 0.306790
## SEMANA34 SEMANA35
## 0.282837 0.343966
## SEMANA36 SEMANA37
## 0.266924 0.333940
## SEMANA38 SEMANA39
## 0.323662 0.280236
## SEMANA40 SEMANA41
## 0.305625 0.174690
## SEMANA42 SEMANA43
## 0.277874 0.267465
## SEMANA44 SEMANA45
## 0.232769 0.224510
## SEMANA46 SEMANA47
## 0.232512 0.235860
## SEMANA48 SEMANA49
## 0.212498 0.209614
## SEMANA50 SEMANA51
## 0.219495 0.267641
## SEMANA52 SEMANA53
## 0.159871 -0.076472
## Feriado_Lunes Feriado_Otro
## -0.355846 -0.270664
## Madre Semana_Santa
## 0.115843 -0.178485
## Viernes_Desp_Quincena_v2 Feria_Flores
## 0.044937 0.047819
##
## Degrees of Freedom: 1460 Total (i.e. Null); 1395 Residual
## Null Deviance: 3880
## Residual Deviance: 2178 AIC: 11050Calculo MSE y RMSE para los datos de entrenamiento
y_tr_pred_glm_m<-predict(glm_fit_m,Train_D_Dataset[,c("Ano_Base","DIA","SEMANA","Feriado_Lunes","Feriado_Otro","Madre","Semana_Santa","Viernes_Desp_Quincena_v2","Feria_Flores")],type="response")
mse_tr_glm_m<-mean((Train_D_Dataset$ACCIDENTES_GRAVES-y_tr_pred_glm_m)^2) # calcula el mse de entrenamiento
RMSE_tr_glm_m = sqrt(mse_tr_glm_m)
mse_tr_glm_m## [1] 95.42817RMSE_tr_glm_m## [1] 9.768734Calculo MSE y RMSE para los datos de validación
y_test_pred_glm_m<-predict(glm_fit_m,Test_D_Dataset[,c("Ano_Base","DIA","SEMANA","Feriado_Lunes","Feriado_Otro","Madre","Semana_Santa","Viernes_Desp_Quincena_v2","Feria_Flores")],type="response")
mse_test_glm_m<-mean((Train_D_Dataset$ACCIDENTES_GRAVES-y_test_pred_glm_m)^2) # calcula el mse de entrenamiento## Warning in Train_D_Dataset$ACCIDENTES_GRAVES - y_test_pred_glm_m: longitud
## de objeto mayor no es múltiplo de la longitud de uno menorRMSE_test_glm_m = sqrt(mse_test_glm_m)
mse_test_glm_m## [1] 216.5946RMSE_test_glm_m## [1] 14.71715Predicción en la muestra
plot_ly (data=Train_D_Dataset,
x = ~FECHA,
y = ~ACCIDENTES_GRAVES,
type = "scatter" ,mode = "lines",
name='Real',
line=list(width=1,color='rgb(205, 12, 24)'))%>%
add_trace(y= ~y_tr_pred_glm_m,
name='Modelo glm',
line=list(width=1,color='rgb(22, 96, 167)'))%>%
layout(title='Total accidentes graves',
xaxis=list(title="Fecha"),
yaxis=list(title="Accidentes graves"),
legend = list(x = 0.75, y = 0.9))Gráfica serie 2018
plot_ly (data=Test_D_Dataset,
x = ~FECHA,
y = ~ACCIDENTES_GRAVES,
type = "scatter" ,mode = "lines",
name='Real',
line=list(width=1,color='rgb(205, 12, 24)'))%>%
add_trace(y= ~y_test_pred_glm_m,
name='Modelo glm',
line=list(width=1,color='rgb(22, 96, 167)'))%>%
layout(title='Total accidentes graves',
xaxis=list(title="Fecha"),
yaxis=list(title="Accidentes graves"),
legend = list(x = 0.75, y = 0.9))Accidentes Leves
glm_fit_sd<-glm(ACCIDENTES_LEVES∼Ano_Base+DIA+SEMANA+Feriado_Lunes+Feriado_Otro+Madre+Semana_Santa+Viernes_Desp_Quincena_v2+Feria_Flores, data=Train_D_Dataset, family = "poisson")
summary(glm_fit_sd)##
## Call:
## glm(formula = ACCIDENTES_LEVES ~ Ano_Base + DIA + SEMANA + Feriado_Lunes +
## Feriado_Otro + Madre + Semana_Santa + Viernes_Desp_Quincena_v2 +
## Feria_Flores, family = "poisson", data = Train_D_Dataset)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -6.4623 -0.8049 -0.0463 0.7879 5.7826
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 3.566375 0.036800 96.913 < 2e-16 ***
## Ano_Base 0.032994 0.007481 4.411 1.03e-05 ***
## DIA2 0.016501 0.013689 1.205 0.22804
## DIA3 -0.034131 0.013839 -2.466 0.01365 *
## DIA4 -0.043615 0.013967 -3.123 0.00179 **
## DIA5 0.059498 0.014400 4.132 3.60e-05 ***
## DIA6 -0.120702 0.014131 -8.542 < 2e-16 ***
## DIA7 -0.779324 0.017117 -45.529 < 2e-16 ***
## SEMANA02 0.231231 0.046740 4.947 7.53e-07 ***
## SEMANA03 0.365275 0.044977 8.121 4.61e-16 ***
## SEMANA04 0.429127 0.044234 9.701 < 2e-16 ***
## SEMANA05 0.422250 0.044301 9.531 < 2e-16 ***
## SEMANA06 0.443281 0.044121 10.047 < 2e-16 ***
## SEMANA07 0.543252 0.043403 12.516 < 2e-16 ***
## SEMANA08 0.455157 0.044027 10.338 < 2e-16 ***
## SEMANA09 0.470804 0.043950 10.712 < 2e-16 ***
## SEMANA10 0.533917 0.043425 12.295 < 2e-16 ***
## SEMANA11 0.495529 0.043870 11.295 < 2e-16 ***
## SEMANA12 0.473432 0.045328 10.445 < 2e-16 ***
## SEMANA13 0.476776 0.044410 10.736 < 2e-16 ***
## SEMANA14 0.532226 0.044751 11.893 < 2e-16 ***
## SEMANA15 0.561218 0.044578 12.590 < 2e-16 ***
## SEMANA16 0.415266 0.045388 9.149 < 2e-16 ***
## SEMANA17 0.555825 0.043325 12.829 < 2e-16 ***
## SEMANA18 0.569384 0.043655 13.043 < 2e-16 ***
## SEMANA19 0.502320 0.044140 11.380 < 2e-16 ***
## SEMANA20 0.524008 0.043545 12.034 < 2e-16 ***
## SEMANA21 0.500812 0.043857 11.419 < 2e-16 ***
## SEMANA22 0.452511 0.044516 10.165 < 2e-16 ***
## SEMANA23 0.499732 0.044076 11.338 < 2e-16 ***
## SEMANA24 0.484160 0.044032 10.996 < 2e-16 ***
## SEMANA25 0.431187 0.044634 9.661 < 2e-16 ***
## SEMANA26 0.392980 0.045004 8.732 < 2e-16 ***
## SEMANA27 0.457483 0.044908 10.187 < 2e-16 ***
## SEMANA28 0.465838 0.043951 10.599 < 2e-16 ***
## SEMANA29 0.515251 0.043946 11.725 < 2e-16 ***
## SEMANA30 0.506661 0.044204 11.462 < 2e-16 ***
## SEMANA31 0.527088 0.048797 10.802 < 2e-16 ***
## SEMANA32 0.559978 0.046302 12.094 < 2e-16 ***
## SEMANA33 0.518888 0.043774 11.854 < 2e-16 ***
## SEMANA34 0.448565 0.044720 10.031 < 2e-16 ***
## SEMANA35 0.446606 0.044139 10.118 < 2e-16 ***
## SEMANA36 0.491971 0.043741 11.247 < 2e-16 ***
## SEMANA37 0.533034 0.043478 12.260 < 2e-16 ***
## SEMANA38 0.551099 0.043299 12.728 < 2e-16 ***
## SEMANA39 0.437909 0.044175 9.913 < 2e-16 ***
## SEMANA40 0.561143 0.043226 12.982 < 2e-16 ***
## SEMANA41 0.452376 0.044048 10.270 < 2e-16 ***
## SEMANA42 0.494565 0.044608 11.087 < 2e-16 ***
## SEMANA43 0.495312 0.043715 11.330 < 2e-16 ***
## SEMANA44 0.512281 0.043633 11.741 < 2e-16 ***
## SEMANA45 0.444380 0.045020 9.871 < 2e-16 ***
## SEMANA46 0.549282 0.043752 12.554 < 2e-16 ***
## SEMANA47 0.518930 0.043916 11.816 < 2e-16 ***
## SEMANA48 0.523360 0.043550 12.017 < 2e-16 ***
## SEMANA49 0.586664 0.043332 13.539 < 2e-16 ***
## SEMANA50 0.587866 0.043386 13.550 < 2e-16 ***
## SEMANA51 0.611326 0.042928 14.241 < 2e-16 ***
## SEMANA52 0.308655 0.045949 6.717 1.85e-11 ***
## SEMANA53 0.168237 0.073074 2.302 0.02132 *
## Feriado_Lunes -0.926258 0.034049 -27.204 < 2e-16 ***
## Feriado_Otro -0.755488 0.041933 -18.017 < 2e-16 ***
## Madre 0.340084 0.083848 4.056 4.99e-05 ***
## Semana_Santa -0.200182 0.032101 -6.236 4.49e-10 ***
## Viernes_Desp_Quincena_v2 0.010337 0.023064 0.448 0.65403
## Feria_Flores 0.098884 0.032066 3.084 0.00204 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 8499.6 on 1460 degrees of freedom
## Residual deviance: 2431.0 on 1395 degrees of freedom
## AIC: 10907
##
## Number of Fisher Scoring iterations: 4glm_fit_sd##
## Call: glm(formula = ACCIDENTES_LEVES ~ Ano_Base + DIA + SEMANA + Feriado_Lunes +
## Feriado_Otro + Madre + Semana_Santa + Viernes_Desp_Quincena_v2 +
## Feria_Flores, family = "poisson", data = Train_D_Dataset)
##
## Coefficients:
## (Intercept) Ano_Base
## 3.56638 0.03299
## DIA2 DIA3
## 0.01650 -0.03413
## DIA4 DIA5
## -0.04362 0.05950
## DIA6 DIA7
## -0.12070 -0.77932
## SEMANA02 SEMANA03
## 0.23123 0.36527
## SEMANA04 SEMANA05
## 0.42913 0.42225
## SEMANA06 SEMANA07
## 0.44328 0.54325
## SEMANA08 SEMANA09
## 0.45516 0.47080
## SEMANA10 SEMANA11
## 0.53392 0.49553
## SEMANA12 SEMANA13
## 0.47343 0.47678
## SEMANA14 SEMANA15
## 0.53223 0.56122
## SEMANA16 SEMANA17
## 0.41527 0.55582
## SEMANA18 SEMANA19
## 0.56938 0.50232
## SEMANA20 SEMANA21
## 0.52401 0.50081
## SEMANA22 SEMANA23
## 0.45251 0.49973
## SEMANA24 SEMANA25
## 0.48416 0.43119
## SEMANA26 SEMANA27
## 0.39298 0.45748
## SEMANA28 SEMANA29
## 0.46584 0.51525
## SEMANA30 SEMANA31
## 0.50666 0.52709
## SEMANA32 SEMANA33
## 0.55998 0.51889
## SEMANA34 SEMANA35
## 0.44856 0.44661
## SEMANA36 SEMANA37
## 0.49197 0.53303
## SEMANA38 SEMANA39
## 0.55110 0.43791
## SEMANA40 SEMANA41
## 0.56114 0.45238
## SEMANA42 SEMANA43
## 0.49456 0.49531
## SEMANA44 SEMANA45
## 0.51228 0.44438
## SEMANA46 SEMANA47
## 0.54928 0.51893
## SEMANA48 SEMANA49
## 0.52336 0.58666
## SEMANA50 SEMANA51
## 0.58787 0.61133
## SEMANA52 SEMANA53
## 0.30866 0.16824
## Feriado_Lunes Feriado_Otro
## -0.92626 -0.75549
## Madre Semana_Santa
## 0.34008 -0.20018
## Viernes_Desp_Quincena_v2 Feria_Flores
## 0.01034 0.09888
##
## Degrees of Freedom: 1460 Total (i.e. Null); 1395 Residual
## Null Deviance: 8500
## Residual Deviance: 2431 AIC: 10910Calculo MSE y RMSE para los datos de entrenamiento
y_tr_pred_glm_sd<-predict(glm_fit_sd,Train_D_Dataset[,c("Ano_Base","DIA","SEMANA","Feriado_Lunes","Feriado_Otro","Madre","Semana_Santa","Viernes_Desp_Quincena_v2","Feria_Flores")],type="response")
mse_tr_glm_sd<-mean((Train_D_Dataset$ACCIDENTES_LEVES-y_tr_pred_glm_sd)^2) # calcula el mse de entrenamiento
RMSE_tr_glm_sd = sqrt(mse_tr_glm_sd)
mse_tr_glm_sd## [1] 82.47408RMSE_tr_glm_sd## [1] 9.081524Calculo MSE y RMSE para los datos de validación
y_test_pred_glm_sd<-predict(glm_fit_sd,Test_D_Dataset[,c("Ano_Base","DIA","SEMANA","Feriado_Lunes","Feriado_Otro","Madre","Semana_Santa","Viernes_Desp_Quincena_v2","Feria_Flores")],type="response")
mse_test_glm_sd<-mean((Train_D_Dataset$ACCIDENTES_LEVES-y_test_pred_glm_sd)^2) # calcula el mse de entrenamiento## Warning in Train_D_Dataset$ACCIDENTES_LEVES - y_test_pred_glm_sd: longitud
## de objeto mayor no es múltiplo de la longitud de uno menorRMSE_test_glm_sd = sqrt(mse_test_glm_sd)
mse_test_glm_sd## [1] 464.2341RMSE_test_glm_sd## [1] 21.54609Predicción en la muestra
plot_ly (data=Train_D_Dataset,
x = ~FECHA,
y = ~ACCIDENTES_LEVES,
type = "scatter" ,mode = "lines",
name='Real',
line=list(width=1,color='rgb(205, 12, 24)'))%>%
add_trace(y= ~y_tr_pred_glm_sd,
name='Modelo glm',
line=list(width=1,color='rgb(22, 96, 167)'))%>%
layout(title='Total accidentes leves',
xaxis=list(title="Fecha"),
yaxis=list(title="Accidentes leves"),
legend = list(x = 0.75, y = 0.9))Gráfica serie 2018
plot_ly (data=Test_D_Dataset,
x = ~FECHA,
y = ~ACCIDENTES_LEVES,
type = "scatter" ,mode = "lines",
name='Real',
line=list(width=1,color='rgb(205, 12, 24)'))%>%
add_trace(y= ~y_test_pred_glm_sd,
name='Modelo glm',
line=list(width=1,color='rgb(22, 96, 167)'))%>%
layout(title='Total accidentes leves',
xaxis=list(title="Fecha"),
yaxis=list(title="Accidentes leves"),
legend = list(x = 0.75, y = 0.9))Tipo_de_accidentes= c("Total Accidentes","Accidentes Graves","Accidentes Leves")
RMSE_Train_glm = round(c(RMSE_tr_glm,RMSE_tr_glm_m,RMSE_tr_glm_sd), 3)
RMSE_Test_glm = round(c(RMSE_test_glm,RMSE_test_glm_m,RMSE_test_glm_sd),3)
Tabla_glm = data.frame (cbind(Tipo_de_accidentes,RMSE_Train_glm,RMSE_Test_glm))
Tabla_glm## Tipo_de_accidentes RMSE_Train_glm RMSE_Test_glm
## 1 Total Accidentes 14.493 33.566
## 2 Accidentes Graves 9.769 14.717
## 3 Accidentes Leves 9.082 21.5464. ARBOLES DE REGRESION
Total Accidentes
trcntrl = trainControl(method="cv", number=10)
caret_tree_fit = caret::train(TOTAL_ACCIDENTES∼Ano_Base+DIA+SEMANA+Feriado_Lunes+Feriado_Otro+Madre+Semana_Santa+Viernes_Desp_Quincena_v2+Feria_Flores,data=Train_D_Dataset,
method = "rpart", trControl = trcntrl,
parms = list(split = "gini"),
preProcess=c("center", "scale"),
tuneLength = 10)## Warning in nominalTrainWorkflow(x = x, y = y, wts = weights, info =
## trainInfo, : There were missing values in resampled performance measures.caret_tree_fit## CART
##
## 1461 samples
## 9 predictor
##
## Pre-processing: centered (65), scaled (65)
## Resampling: Cross-Validated (10 fold)
## Summary of sample sizes: 1314, 1314, 1315, 1314, 1317, 1315, ...
## Resampling results across tuning parameters:
##
## cp RMSE Rsquared MAE
## 0.002809420 16.39978 0.6057978 12.59500
## 0.003426222 16.40104 0.6057009 12.61132
## 0.004088523 16.42877 0.6043830 12.62974
## 0.004296741 16.45966 0.6032039 12.66308
## 0.009015951 16.55411 0.5986592 12.71343
## 0.015577060 16.80019 0.5860808 12.89773
## 0.019519115 17.20313 0.5654403 13.20829
## 0.074166850 18.36318 0.5051650 13.77296
## 0.124936739 20.21321 0.3988816 14.92716
## 0.361935625 24.29677 0.3072139 18.73530
##
## RMSE was used to select the optimal model using the smallest value.
## The final value used for the model was cp = 0.00280942.Calculo MSE y RMSE para los datos de entrenamiento
y_tr_pred_tree<-predict(caret_tree_fit,Train_D_Dataset[,c("Ano_Base","DIA","SEMANA","Feriado_Lunes","Feriado_Otro","Madre","Semana_Santa","Viernes_Desp_Quincena_v2","Feria_Flores")])
mse_tr_tree<-mean((Train_D_Dataset$TOTAL_ACCIDENTES-y_tr_pred_tree)^2) # calcula el mse de entrenamiento
RMSE_tr_tree = sqrt(mse_tr_tree)
mse_tr_tree## [1] 260.5989RMSE_tr_tree## [1] 16.14308Calculo MSE y RMSE para los datos de validación
y_test_pred_tree<-predict(caret_tree_fit,Test_D_Dataset[,c("Ano_Base","DIA","SEMANA","Feriado_Lunes","Feriado_Otro","Madre","Semana_Santa","Viernes_Desp_Quincena_v2","Feria_Flores")])
mse_test_tree<-mean((Test_D_Dataset$TOTAL_ACCIDENTES-y_test_pred_tree)^2) # calcula el mse de entrenamiento
RMSE_test_tree = sqrt(mse_test_tree)
mse_test_tree## [1] 266.3289RMSE_test_tree## [1] 16.31959Predicción en la muestra
plot_ly (data=Train_D_Dataset,
x = ~FECHA,
y = ~TOTAL_ACCIDENTES,
type = "scatter" ,mode = "lines",
name='Real',
line=list(width=1,color='rgb(205, 12, 24)'))%>%
add_trace(y= ~y_tr_pred_tree,
name='Modelo tree',
line=list(width=1,color='rgb(22, 96, 167)'))%>%
layout(title='Total accidentes',
xaxis=list(title="Fecha"),
yaxis=list(title="Accidentes"),
legend = list(x = 0.75, y = 0.9))plot_ly (data=Test_D_Dataset,
x = ~FECHA,
y = ~TOTAL_ACCIDENTES,
type = "scatter" ,mode = "lines",
name='Real',
line=list(width=1,color='rgb(205, 12, 24)'))%>%
add_trace(y= ~y_test_pred_tree,
name='Modelo tree',
line=list(width=1,color='rgb(22, 96, 167)'))%>%
layout(title='Total accidentes',
xaxis=list(title="Fecha"),
yaxis=list(title="Accidentes"),
legend = list(x = 0.75, y = 0.9))Accidentes graves
trcntrl = trainControl(method="cv", number=10)
caret_tree_fit_m = caret::train(ACCIDENTES_GRAVES∼Ano_Base+DIA+SEMANA+Feriado_Lunes+Feriado_Otro+Madre+Semana_Santa+Viernes_Desp_Quincena_v2+Feria_Flores,data=Train_D_Dataset,
method = "rpart", trControl = trcntrl,
parms = list(split = "gini"),
preProcess=c("center", "scale"),
tuneLength = 10)## Warning in nominalTrainWorkflow(x = x, y = y, wts = weights, info =
## trainInfo, : There were missing values in resampled performance measures.caret_tree_fit_m## CART
##
## 1461 samples
## 9 predictor
##
## Pre-processing: centered (65), scaled (65)
## Resampling: Cross-Validated (10 fold)
## Summary of sample sizes: 1315, 1315, 1314, 1314, 1314, 1317, ...
## Resampling results across tuning parameters:
##
## cp RMSE Rsquared MAE
## 0.004304283 10.58815 0.3319637 8.297331
## 0.004855980 10.66021 0.3228533 8.344004
## 0.005581935 10.63185 0.3248362 8.307734
## 0.007392616 10.62566 0.3250349 8.298521
## 0.007539749 10.63250 0.3244980 8.304343
## 0.009846728 10.69329 0.3169611 8.326118
## 0.016928511 10.78241 0.3051460 8.378719
## 0.047322798 11.07134 0.2684788 8.520850
## 0.069415275 11.47112 0.2149861 8.840378
## 0.185351723 12.60088 0.1267916 9.810758
##
## RMSE was used to select the optimal model using the smallest value.
## The final value used for the model was cp = 0.004304283.Calculo MSE y RMSE para los datos de entrenamiento
y_tr_pred_tree_m<-predict(caret_tree_fit_m,Train_D_Dataset[,c("Ano_Base","DIA","SEMANA","Feriado_Lunes","Feriado_Otro","Madre","Semana_Santa","Viernes_Desp_Quincena_v2","Feria_Flores")])
mse_tr_tree_m<-mean((Train_D_Dataset$ACCIDENTES_GRAVES-y_tr_pred_tree_m)^2) # calcula el mse de entrenamiento
RMSE_tr_tree_m = sqrt(mse_tr_tree_m)
mse_tr_tree_m## [1] 107.9408RMSE_tr_tree_m## [1] 10.38946Calculo MSE y RMSE para los datos de validación
y_test_pred_tree_m<-predict(caret_tree_fit_m,Test_D_Dataset[,c("Ano_Base","DIA","SEMANA","Feriado_Lunes","Feriado_Otro","Madre","Semana_Santa","Viernes_Desp_Quincena_v2","Feria_Flores")])
mse_test_tree_m<-mean((Test_D_Dataset$ACCIDENTES_GRAVES-y_test_pred_tree_m)^2) # calcula el mse de entrenamiento
RMSE_test_tree_m = sqrt(mse_test_tree_m)
mse_test_tree_m## [1] 119.1989RMSE_test_tree_m## [1] 10.91782Predicción en la muestra
plot_ly (data=Train_D_Dataset,
x = ~FECHA,
y = ~ACCIDENTES_GRAVES,
type = "scatter" ,mode = "lines",
name='Real',
line=list(width=1,color='rgb(205, 12, 24)'))%>%
add_trace(y= ~y_tr_pred_tree_m,
name='Modelo tree',
line=list(width=1,color='rgb(22, 96, 167)'))%>%
layout(title='Total accidentes graves',
xaxis=list(title="Fecha"),
yaxis=list(title="Accidentes"),
legend = list(x = 0.75, y = 0.9))plot_ly (data=Test_D_Dataset,
x = ~FECHA,
y = ~ACCIDENTES_GRAVES,
type = "scatter" ,mode = "lines",
name='Real',
line=list(width=1,color='rgb(205, 12, 24)'))%>%
add_trace(y= ~y_test_pred_tree_m,
name='Modelo tree',
line=list(width=1,color='rgb(22, 96, 167)'))%>%
layout(title='Total accidentes graves',
xaxis=list(title="Fecha"),
yaxis=list(title="Accidentes"),
legend = list(x = 0.75, y = 0.9))Accidentes leves
trcntrl = trainControl(method="cv", number=10)
caret_tree_fit_sd = caret::train(ACCIDENTES_LEVES∼Ano_Base+DIA+SEMANA+Feriado_Lunes+Feriado_Otro+Madre+Semana_Santa+Viernes_Desp_Quincena_v2+Feria_Flores,data=Train_D_Dataset,
method = "rpart", trControl = trcntrl,
parms = list(split = "gini"),
preProcess=c("center", "scale"),
tuneLength = 10)## Warning in nominalTrainWorkflow(x = x, y = y, wts = weights, info =
## trainInfo, : There were missing values in resampled performance measures.caret_tree_fit_sd## CART
##
## 1461 samples
## 9 predictor
##
## Pre-processing: centered (65), scaled (65)
## Resampling: Cross-Validated (10 fold)
## Summary of sample sizes: 1315, 1315, 1314, 1315, 1316, 1315, ...
## Resampling results across tuning parameters:
##
## cp RMSE Rsquared MAE
## 0.002017390 10.20554 0.6119329 7.930299
## 0.003376782 10.22313 0.6100858 7.968754
## 0.003692924 10.24844 0.6082667 8.007125
## 0.004346199 10.25259 0.6077759 8.012660
## 0.006899134 10.26635 0.6066895 8.024890
## 0.010072870 10.38657 0.5972172 8.101447
## 0.021713587 10.54834 0.5846967 8.241405
## 0.068542816 11.13427 0.5365761 8.578525
## 0.125802593 12.52647 0.4165720 9.419984
## 0.381699929 15.25290 0.3127356 11.939687
##
## RMSE was used to select the optimal model using the smallest value.
## The final value used for the model was cp = 0.00201739.Calculo MSE y RMSE para los datos de entrenamiento
y_tr_pred_tree_sd<-predict(caret_tree_fit_sd,Train_D_Dataset[,c("Ano_Base","DIA","SEMANA","Feriado_Lunes","Feriado_Otro","Madre","Semana_Santa","Viernes_Desp_Quincena_v2","Feria_Flores")])
mse_tr_tree_sd<-mean((Train_D_Dataset$ACCIDENTES_LEVES-y_tr_pred_tree_sd)^2) # calcula el mse de entrenamiento
RMSE_tr_tree_sd = sqrt(mse_tr_tree_sd)
mse_tr_tree_sd## [1] 100.4275RMSE_tr_tree_sd## [1] 10.02135Calculo MSE y RMSE para los datos de validación
y_test_pred_tree_sd<-predict(caret_tree_fit_sd,Test_D_Dataset[,c("Ano_Base","DIA","SEMANA","Feriado_Lunes","Feriado_Otro","Madre","Semana_Santa","Viernes_Desp_Quincena_v2","Feria_Flores")])
mse_test_tree_sd<-mean((Test_D_Dataset$ACCIDENTES_LEVES-y_test_pred_tree_sd)^2) # calcula el mse de entrenamiento
RMSE_test_tree_sd = sqrt(mse_test_tree_sd)
mse_test_tree_sd## [1] 124.1118RMSE_test_tree_sd## [1] 11.14055Predicción en la muestra
plot_ly (data=Train_D_Dataset,
x = ~FECHA,
y = ~ACCIDENTES_LEVES,
type = "scatter" ,mode = "lines",
name='Real',
line=list(width=1,color='rgb(205, 12, 24)'))%>%
add_trace(y= ~y_tr_pred_tree_sd,
name='Modelo tree',
line=list(width=1,color='rgb(22, 96, 167)'))%>%
layout(title='Total accidentes leves',
xaxis=list(title="Fecha"),
yaxis=list(title="Accidentes leves"),
legend = list(x = 0.75, y = 0.9))plot_ly (data=Test_D_Dataset,
x = ~FECHA,
y = ~ACCIDENTES_LEVES,
type = "scatter" ,mode = "lines",
name='Real',
line=list(width=1,color='rgb(205, 12, 24)'))%>%
add_trace(y= ~y_test_pred_tree_sd,
name='Modelo tree',
line=list(width=1,color='rgb(22, 96, 167)'))%>%
layout(title='Total accidentes leves',
xaxis=list(title="Fecha"),
yaxis=list(title="Accidentes leves"),
legend = list(x = 0.75, y = 0.9))Resumen Modelos Árboles de regresión para los diferentes tipos de accidente
Tipo_de_accidentes= c("Total Accidentes","Accidentes Graves","Accidentes Leves")
RMSE_Train_tree = round(c(RMSE_tr_tree,RMSE_tr_tree_m,RMSE_tr_tree_sd), 3)
RMSE_Test_tree = round(c(RMSE_test_tree,RMSE_test_tree_m,RMSE_test_tree_sd),3)
Tabla_tree = data.frame (cbind(Tipo_de_accidentes,RMSE_Train_tree,RMSE_Test_tree))
Tabla_tree## Tipo_de_accidentes RMSE_Train_tree RMSE_Test_tree
## 1 Total Accidentes 16.143 16.32
## 2 Accidentes Graves 10.389 10.918
## 3 Accidentes Leves 10.021 11.1415. BOSQUE ALEATORIO
Total Accidentes
trcntrl = trainControl(method="cv", number=10)
caret_rf_fit = caret::train(TOTAL_ACCIDENTES∼Ano_Base+DIA+SEMANA+Feriado_Lunes+Feriado_Otro+Madre+Semana_Santa+Viernes_Desp_Quincena_v2+Feria_Flores, data=Train_D_Dataset,
method = "rf", trControl = trcntrl,
prox=TRUE,allowParallel=TRUE)summary(caret_rf_fit)## Length Class Mode
## call 6 -none- call
## type 1 -none- character
## predicted 1461 -none- numeric
## mse 500 -none- numeric
## rsq 500 -none- numeric
## oob.times 1461 -none- numeric
## importance 65 -none- numeric
## importanceSD 0 -none- NULL
## localImportance 0 -none- NULL
## proximity 2134521 -none- numeric
## ntree 1 -none- numeric
## mtry 1 -none- numeric
## forest 11 -none- list
## coefs 0 -none- NULL
## y 1461 -none- numeric
## test 0 -none- NULL
## inbag 0 -none- NULL
## xNames 65 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 1 -none- logical
## param 2 -none- listcaret_rf_fit## Random Forest
##
## 1461 samples
## 9 predictor
##
## No pre-processing
## Resampling: Cross-Validated (10 fold)
## Summary of sample sizes: 1314, 1315, 1317, 1315, 1314, 1315, ...
## Resampling results across tuning parameters:
##
## mtry RMSE Rsquared MAE
## 2 19.71481 0.6110435 15.64526
## 33 17.34363 0.5791274 13.48688
## 65 17.62020 0.5709986 13.72791
##
## RMSE was used to select the optimal model using the smallest value.
## The final value used for the model was mtry = 33.plot(caret_rf_fit)
Calculo MSE y RMSE para los datos de entrenamiento
y_tr_pred_rf<-predict(caret_rf_fit,Train_D_Dataset[,c("Ano_Base","DIA","SEMANA","Feriado_Lunes","Feriado_Otro","Madre","Semana_Santa","Viernes_Desp_Quincena_v2","Feria_Flores")])
mse_tr_rf<-mean((Train_D_Dataset$TOTAL_ACCIDENTES-y_tr_pred_rf)^2) # calcula el mse de entrenamiento
RMSE_tr_rf = sqrt(mse_tr_rf)
mse_tr_rf## [1] 122.1749RMSE_tr_rf## [1] 11.05328Calculo MSE y RMSE para los datos de validación
y_test_pred_rf<-predict(caret_rf_fit,Test_D_Dataset[,c("Ano_Base","DIA","SEMANA","Feriado_Lunes","Feriado_Otro","Madre","Semana_Santa","Viernes_Desp_Quincena_v2","Feria_Flores")])
mse_test_rf<-mean((Test_D_Dataset$TOTAL_ACCIDENTES-y_test_pred_rf)^2) # calcula el mse de entrenamiento
RMSE_test_rf = sqrt(mse_test_rf)
mse_test_rf## [1] 285.1705RMSE_test_rf## [1] 16.88699Predicción en la muestra
plot_ly (data=Train_D_Dataset,
x = ~FECHA,
y = ~TOTAL_ACCIDENTES,
type = "scatter" ,mode = "lines",
name='Real',
line=list(width=1,color='rgb(205, 12, 24)'))%>%
add_trace(y= ~y_tr_pred_rf,
name='Modelo rf',
line=list(width=1,color='rgb(22, 96, 167)'))%>%
layout(title='Total accidentes',
xaxis=list(title="Fecha"),
yaxis=list(title="Accidentes"),
legend = list(x = 0.75, y = 0.9))Gráfica serie 2018
plot_ly (data=Test_D_Dataset,
x = ~FECHA,
y = ~TOTAL_ACCIDENTES,
type = "scatter" ,mode = "lines",
name='Real',
line=list(width=1,color='rgb(205, 12, 24)'))%>%
add_trace(y= ~y_test_pred_rf,
name='Modelo rf',
line=list(width=1,color='rgb(22, 96, 167)'))%>%
layout(title='Total accidentes',
xaxis=list(title="Fecha"),
yaxis=list(title="Accidentes"),
legend = list(x = 0.75, y = 0.9))Accidentes Graves
trcntrl = trainControl(method="cv", number=10)
caret_rf_fit_m = caret::train(ACCIDENTES_GRAVES∼Ano_Base+DIA+SEMANA+Feriado_Lunes+Feriado_Otro+Madre+Semana_Santa+Viernes_Desp_Quincena_v2+Feria_Flores, data=Train_D_Dataset,
method = "rf", trControl = trcntrl,
prox=TRUE,allowParallel=TRUE)summary(caret_rf_fit_m)## Length Class Mode
## call 6 -none- call
## type 1 -none- character
## predicted 1461 -none- numeric
## mse 500 -none- numeric
## rsq 500 -none- numeric
## oob.times 1461 -none- numeric
## importance 65 -none- numeric
## importanceSD 0 -none- NULL
## localImportance 0 -none- NULL
## proximity 2134521 -none- numeric
## ntree 1 -none- numeric
## mtry 1 -none- numeric
## forest 11 -none- list
## coefs 0 -none- NULL
## y 1461 -none- numeric
## test 0 -none- NULL
## inbag 0 -none- NULL
## xNames 65 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 1 -none- logical
## param 2 -none- listcaret_rf_fit_m## Random Forest
##
## 1461 samples
## 9 predictor
##
## No pre-processing
## Resampling: Cross-Validated (10 fold)
## Summary of sample sizes: 1314, 1314, 1316, 1315, 1313, 1316, ...
## Resampling results across tuning parameters:
##
## mtry RMSE Rsquared MAE
## 2 11.18234 0.3499046 8.752868
## 33 11.22344 0.2942227 8.735058
## 65 11.46086 0.2818605 8.909610
##
## RMSE was used to select the optimal model using the smallest value.
## The final value used for the model was mtry = 2.plot(caret_rf_fit_m)
Calculo MSE y RMSE para los datos de entrenamiento
y_tr_pred_rf_m<-predict(caret_rf_fit_m,Train_D_Dataset[,c("Ano_Base","DIA","SEMANA","Feriado_Lunes","Feriado_Otro","Madre","Semana_Santa","Viernes_Desp_Quincena_v2","Feria_Flores")])
mse_tr_rf_m<-mean((Train_D_Dataset$ACCIDENTES_GRAVES-y_tr_pred_rf_m)^2) # calcula el mse de entrenamiento
RMSE_tr_rf_m = sqrt(mse_tr_rf_m)
mse_tr_rf_m## [1] 116.8217RMSE_tr_rf_m## [1] 10.80841Calculo MSE y RMSE para los datos de validación
y_test_pred_rf_m<-predict(caret_rf_fit_m,Test_D_Dataset[,c("Ano_Base","DIA","SEMANA","Feriado_Lunes","Feriado_Otro","Madre","Semana_Santa","Viernes_Desp_Quincena_v2","Feria_Flores")])
mse_test_rf_m<-mean((Test_D_Dataset$ACCIDENTES_GRAVES-y_test_pred_rf_m)^2) # calcula el mse de entrenamiento
RMSE_test_rf_m = sqrt(mse_test_rf_m)
mse_test_rf_m## [1] 144.8967RMSE_test_rf_m## [1] 12.03731Predicción en la muestra
plot_ly (data=Train_D_Dataset,
x = ~FECHA,
y = ~ACCIDENTES_GRAVES,
type = "scatter" ,mode = "lines",
name='Real',
line=list(width=1,color='rgb(205, 12, 24)'))%>%
add_trace(y= ~y_tr_pred_rf_m,
name='Modelo rf',
line=list(width=1,color='rgb(22, 96, 167)'))%>%
layout(title='Total accidentes graves',
xaxis=list(title="Fecha"),
yaxis=list(title="Accidentes graves"),
legend = list(x = 0.75, y = 0.9))Gráfica serie 2018
plot_ly (data=Test_D_Dataset,
x = ~FECHA,
y = ~ACCIDENTES_GRAVES,
type = "scatter" ,mode = "lines",
name='Real',
line=list(width=1,color='rgb(205, 12, 24)'))%>%
add_trace(y= ~y_test_pred_rf_m,
name='Modelo rf',
line=list(width=1,color='rgb(22, 96, 167)'))%>%
layout(title='Total accidentes graves',
xaxis=list(title="Fecha"),
yaxis=list(title="Accidentes graves"),
legend = list(x = 0.75, y = 0.9))Accidentes Leves
trcntrl = trainControl(method="cv", number=10)
caret_rf_fit_sd = caret::train(ACCIDENTES_LEVES∼Ano_Base+DIA+SEMANA+Feriado_Lunes+Feriado_Otro+Madre+Semana_Santa+Viernes_Desp_Quincena_v2+Feria_Flores, data=Train_D_Dataset,
method = "rf", trControl = trcntrl,
prox=TRUE,allowParallel=TRUE)summary(caret_rf_fit_sd)## Length Class Mode
## call 6 -none- call
## type 1 -none- character
## predicted 1461 -none- numeric
## mse 500 -none- numeric
## rsq 500 -none- numeric
## oob.times 1461 -none- numeric
## importance 65 -none- numeric
## importanceSD 0 -none- NULL
## localImportance 0 -none- NULL
## proximity 2134521 -none- numeric
## ntree 1 -none- numeric
## mtry 1 -none- numeric
## forest 11 -none- list
## coefs 0 -none- NULL
## y 1461 -none- numeric
## test 0 -none- NULL
## inbag 0 -none- NULL
## xNames 65 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 1 -none- logical
## param 2 -none- listcaret_rf_fit_sd## Random Forest
##
## 1461 samples
## 9 predictor
##
## No pre-processing
## Resampling: Cross-Validated (10 fold)
## Summary of sample sizes: 1316, 1313, 1314, 1314, 1317, 1315, ...
## Resampling results across tuning parameters:
##
## mtry RMSE Rsquared MAE
## 2 12.37653 0.6118432 10.061618
## 33 10.98192 0.5694633 8.603879
## 65 11.17931 0.5596107 8.761452
##
## RMSE was used to select the optimal model using the smallest value.
## The final value used for the model was mtry = 33.plot(caret_rf_fit_sd)
Calculo MSE y RMSE para los datos de entrenamiento
y_tr_pred_rf_sd<-predict(caret_rf_fit_sd,Train_D_Dataset[,c("Ano_Base","DIA","SEMANA","Feriado_Lunes","Feriado_Otro","Madre","Semana_Santa","Viernes_Desp_Quincena_v2","Feria_Flores")])
mse_tr_rf_sd<-mean((Train_D_Dataset$ACCIDENTES_LEVES-y_tr_pred_rf_sd)^2) # calcula el mse de entrenamiento
RMSE_tr_rf_sd = sqrt(mse_tr_rf_sd)
mse_tr_rf_sd## [1] 48.48793RMSE_tr_rf_sd## [1] 6.963327Calculo MSE y RMSE para los datos de validación
y_test_pred_rf_sd<-predict(caret_rf_fit_sd,Test_D_Dataset[,c("Ano_Base","DIA","SEMANA","Feriado_Lunes","Feriado_Otro","Madre","Semana_Santa","Viernes_Desp_Quincena_v2","Feria_Flores")])
mse_test_rf_sd<-mean((Test_D_Dataset$ACCIDENTES_LEVES-y_test_pred_rf_sd)^2) # calcula el mse de entrenamiento
RMSE_test_rf_sd = sqrt(mse_test_rf_sd)
mse_test_rf_sd## [1] 125.306RMSE_test_rf_sd## [1] 11.19401Predicción en la muestra
plot_ly (data=Train_D_Dataset,
x = ~FECHA,
y = ~ACCIDENTES_LEVES,
type = "scatter" ,mode = "lines",
name='Real',
line=list(width=1,color='rgb(205, 12, 24)'))%>%
add_trace(y= ~y_tr_pred_rf_sd,
name='Modelo rf',
line=list(width=1,color='rgb(22, 96, 167)'))%>%
layout(title='Total accidentes leves',
xaxis=list(title="Fecha"),
yaxis=list(title="Accidentes leves"),
legend = list(x = 0.75, y = 0.9))Gráfica serie 2018
plot_ly (data=Test_D_Dataset,
x = ~FECHA,
y = ~ACCIDENTES_LEVES,
type = "scatter" ,mode = "lines",
name='Real',
line=list(width=1,color='rgb(205, 12, 24)'))%>%
add_trace(y= ~y_test_pred_rf_sd,
name='Modelo rf',
line=list(width=1,color='rgb(22, 96, 167)'))%>%
layout(title='Total accidentes leves',
xaxis=list(title="Fecha"),
yaxis=list(title="Accidentes leves"),
legend = list(x = 0.75, y = 0.9))Resumen Modelos Random Forest para los diferentes tipos de accidente
Tipo_de_accidentes= c("Total Accidentes","Total Graves","Total Leves")
RMSE_Train_rf = round(c(RMSE_tr_rf,RMSE_tr_rf_m,RMSE_tr_rf_sd), 3)
RMSE_Test_rf = round(c(RMSE_test_rf,RMSE_test_rf_m,RMSE_test_rf_sd),3)
Tabla_rf = data.frame (cbind(Tipo_de_accidentes,RMSE_Train_rf,RMSE_Test_rf))
Tabla_rf## Tipo_de_accidentes RMSE_Train_rf RMSE_Test_rf
## 1 Total Accidentes 11.053 16.887
## 2 Total Graves 10.808 12.037
## 3 Total Leves 6.963 11.194ELECCION DEL MODELO
1. Elección del modelo para el total de accidentes
Comparación en el entrenamiento
comparacion_tr<-data.frame(FECHA=Total_Dataset_Freq$FECHA[Total_Dataset_Freq$FECHA<="2017-12-31"],
ACCIDENTES=Total_Dataset_Freq$TOTAL_ACCIDENTES[Total_Dataset_Freq$FECHA<="2017-12-31"],
lm= y_tr_pred_lm,
knn= y_tr_pred_knn,
glm=y_tr_pred_glm ,
arbol=y_tr_pred_tree,
rf=y_tr_pred_rf)plot_ly (data=comparacion_tr,
x = ~FECHA,
y = ~ACCIDENTES,
type = "scatter" ,mode = "lines",
name='Real',
line=list(width=1,color='rgb(205, 12, 24)'))%>%
add_trace(y= ~lm,
name='lm',
line=list(width=1,color= "blue"))%>%
add_trace(y= ~knn,
name='knn',
line=list(width=1,color="red"))%>%
add_trace(y= ~glm,
name='Modelo Poisson',
line=list(width=1,color='rgb(22, 96, 167)'))%>%
add_trace(y= ~arbol,
name='Árbol',
line=list(width=1,color="green"))%>%
add_trace(y= ~rf,
name='Bosque',
line=list(width=1,color='rgb(255, 51, 153)'))%>%
layout(title='Total accidentes (Entrenamiento)',
xaxis=list(title="Fecha"),
yaxis=list(title="Accidentes"),
legend = list(x = 1, y = 0.9))Comparación en la validación
comparacion_vl<-data.frame(FECHA=Test_D_Dataset$FECHA,
ACCIDENTES=Test_D_Dataset$TOTAL_ACCIDENTES,
lm= y_test_pred_lm,
knn= y_test_pred_knn,
glm=y_test_pred_glm ,
arbol=y_test_pred_tree,
rf=y_test_pred_rf)plot_ly (data=comparacion_vl,
x = ~FECHA,
y = ~ACCIDENTES,
type = "scatter" ,mode = "lines",
name='Real',
line=list(width=1,color='rgb(205, 12, 24)'))%>%
add_trace(y= ~lm,
name='lm',
line=list(width=1,color= "blue"))%>%
add_trace(y= ~knn,
name='knn',
line=list(width=1,color="red"))%>%
add_trace(y= ~glm,
name='Modelo Poisson',
line=list(width=1,color='rgb(22, 96, 167)'))%>%
add_trace(y= ~arbol,
name='Árbol',
line=list(width=1,color="green"))%>%
add_trace(y= ~rf,
name='Bosque',
line=list(width=1,color='rgb(255, 51, 153)'))%>%
layout(title='Total Accidentes (Validación)',
xaxis=list(title="Fecha"),
yaxis=list(title="Accidentes"),
legend = list(x = 1, y = 0.9))Comparación con los RMSE:
Entrenamiento<-round(c(RMSE_tr_lm,RMSE_tr_knn,RMSE_tr_glm,RMSE_tr_tree,RMSE_tr_rf),3)
Validacion<-round(c(RMSE_test_lm,RMSE_test_knn,RMSE_test_glm,RMSE_test_tree,RMSE_test_rf),3)
nombres<-c("lm","knn","glm","árbol","bosque")
ResultadosRMSE<-data.frame(Entrenamiento=Entrenamiento,Validacion=Validacion)
rownames(ResultadosRMSE)<-nombresCálculo de la variación
ResultadosRMSE$Por_variacion<-((ResultadosRMSE$Validacion-ResultadosRMSE$Entrenamiento)/ResultadosRMSE$Entrenamiento)*100
ResultadosRMSE## Entrenamiento Validacion Por_variacion
## lm 14.701 16.026 9.012992
## knn 17.492 20.910 19.540361
## glm 14.493 33.566 131.601463
## árbol 16.143 16.320 1.096450
## bosque 11.053 16.887 52.7820502. Elección del modelo para Accidentes graves
Comparación en el entrenamiento
comparacion_tr<-data.frame(FECHA=Total_Dataset_Freq$FECHA[Total_Dataset_Freq$FECHA<="2017-12-31"],
ACCIDENTESG=Total_Dataset_Freq$ACCIDENTES_GRAVES[Total_Dataset_Freq$FECHA<="2017-12-31"],
lm= y_tr_pred_lm_m,
knn= y_tr_pred_knn_m,
glm=y_tr_pred_glm_m,
arbol=y_tr_pred_tree_m,
rf=y_tr_pred_rf_m)plot_ly (data=comparacion_tr,
x = ~FECHA,
y = ~ACCIDENTESG,
type = "scatter" ,mode = "lines",
name='Real',
line=list(width=1,color='rgb(205, 12, 24)'))%>%
add_trace(y= ~lm,
name='lm',
line=list(width=1,color= "blue"))%>%
add_trace(y= ~knn,
name='knn',
line=list(width=1,color="red"))%>%
add_trace(y= ~glm,
name='Modelo Poisson',
line=list(width=1,color='rgb(22, 96, 167)'))%>%
add_trace(y= ~arbol,
name='Árbo',
line=list(width=1,color="green"))%>%
add_trace(y= ~rf,
name='Bosque',
line=list(width=1,color='rgb(255, 51, 153)'))%>%
layout(title='Accidentes graves (Entrenamiento)',
xaxis=list(title="Fecha"),
yaxis=list(title="Accidentes"),
legend = list(x = 1, y = 0.9))Comparación en la validación
comparacion_vl<-data.frame(FECHA=Test_D_Dataset$FECHA,
ACCIDENTESG=Test_D_Dataset$ACCIDENTES_GRAVES,
lm= y_test_pred_lm_m,
knn= y_test_pred_knn_m,
glm=y_test_pred_glm_m,
arbol=y_test_pred_tree_m,
rf=y_test_pred_rf_m)plot_ly (data=comparacion_vl,
x = ~FECHA,
y = ~ACCIDENTESG,
type = "scatter" ,mode = "lines",
name='Real',
line=list(width=1,color='rgb(205, 12, 24)'))%>%
add_trace(y= ~lm,
name='lm',
line=list(width=1,color= "blue"))%>%
add_trace(y= ~knn,
name='knn',
line=list(width=1,color="red"))%>%
add_trace(y= ~glm,
name='Modelo Poisson',
line=list(width=1,color='rgb(22, 96, 167)'))%>%
add_trace(y= ~arbol,
name='Árbol',
line=list(width=1,color="green"))%>%
add_trace(y= ~rf,
name='Bosque',
line=list(width=1,color='rgb(255, 51, 153)'))%>%
layout(title='Accidentes graves (Validación)',
xaxis=list(title="Fecha"),
yaxis=list(title="Accidentes"),
legend = list(x = 1, y = 0.9))Comparación con los RMSE:
Entrenamiento<-round(c(RMSE_tr_lm_m,RMSE_tr_knn_m,RMSE_tr_glm_m,RMSE_tr_tree_m,RMSE_tr_rf_m),3)
Validacion<-round(c(RMSE_test_lm_m,RMSE_test_knn_m,RMSE_test_glm_m,RMSE_test_tree_m,RMSE_test_rf_m),3)
nombres<-c("lm","knn","glm","árbol","bosque")
ResultadosRMSE<-data.frame(Entrenamiento=Entrenamiento,Validacion=Validacion)
rownames(ResultadosRMSE)<-nombresCálculo de la variación
ResultadosRMSE$Por_variacion<-((ResultadosRMSE$Validacion-ResultadosRMSE$Entrenamiento)/ResultadosRMSE$Entrenamiento)*100
ResultadosRMSE## Entrenamiento Validacion Por_variacion
## lm 9.816 11.337 15.495110
## knn 9.814 13.416 36.702670
## glm 9.769 14.717 50.650015
## árbol 10.389 10.918 5.091924
## bosque 10.808 12.037 11.3712073. Elección del modelo para Accidentes leves
Comparación en el entrenamiento
comparacion_tr<-data.frame(FECHA=Total_Dataset_Freq$FECHA[Total_Dataset_Freq$FECHA<="2017-12-31"],
ACCIDENTESL=Total_Dataset_Freq$ACCIDENTES_LEVES[Total_Dataset_Freq$FECHA<="2017-12-31"],
lm= y_tr_pred_lm_sd,
knn= y_tr_pred_knn_sd,
glm=y_tr_pred_glm_sd,
arbol=y_tr_pred_tree_sd,
rf=y_tr_pred_rf_sd)plot_ly (data=comparacion_tr,
x = ~FECHA,
y = ~ACCIDENTESL,
type = "scatter" ,mode = "lines",
name='Real',
line=list(width=1,color='rgb(205, 12, 24)'))%>%
add_trace(y= ~lm,
name='lm',
line=list(width=1,color= "blue"))%>%
add_trace(y= ~knn,
name='knn',
line=list(width=1,color="red"))%>%
add_trace(y= ~glm,
name='Modelo Poisson',
line=list(width=1,color='rgb(22, 96, 167)'))%>%
add_trace(y= ~arbol,
name='Árbo',
line=list(width=1,color="green"))%>%
add_trace(y= ~rf,
name='Bosque',
line=list(width=1,color='rgb(255, 51, 153)'))%>%
layout(title='Accidentes leves (Entrenamiento)',
xaxis=list(title="Fecha"),
yaxis=list(title="Accidentes"),
legend = list(x = 1, y = 0.9))Comparación en la validación
comparacion_vl<-data.frame(FECHA=Test_D_Dataset$FECHA,
ACCIDENTESL=Test_D_Dataset$ACCIDENTES_GRAVES,
lm= y_test_pred_lm_sd,
knn= y_test_pred_knn_sd,
glm=y_test_pred_glm_sd,
arbol=y_test_pred_tree_sd,
rf=y_test_pred_rf_sd)plot_ly (data=comparacion_vl,
x = ~FECHA,
y = ~ACCIDENTESL,
type = "scatter" ,mode = "lines",
name='Real',
line=list(width=1,color='rgb(205, 12, 24)'))%>%
add_trace(y= ~lm,
name='lm',
line=list(width=1,color= "blue"))%>%
add_trace(y= ~knn,
name='knn',
line=list(width=1,color="red"))%>%
add_trace(y= ~glm,
name='Modelo Poisson',
line=list(width=1,color='rgb(22, 96, 167)'))%>%
add_trace(y= ~arbol,
name='Árbol',
line=list(width=1,color="green"))%>%
add_trace(y= ~rf,
name='Bosque',
line=list(width=1,color='rgb(255, 51, 153)'))%>%
layout(title='Accidentes leves (Validación)',
xaxis=list(title="Fecha"),
yaxis=list(title="Accidentes"),
legend = list(x = 1, y = 0.9))Comparación con los RMSE:
Entrenamiento<-round(c(RMSE_tr_lm_sd,RMSE_tr_knn_sd,RMSE_tr_glm_sd,RMSE_tr_tree_sd,RMSE_tr_rf_sd),3)
Validacion<-round(c(RMSE_test_lm_sd,RMSE_test_knn_sd,RMSE_test_glm_sd,RMSE_test_tree_sd,RMSE_test_rf_sd),3)
nombres<-c("lm","knn","glm","árbol","bosque")
ResultadosRMSE<-data.frame(Entrenamiento=Entrenamiento,Validacion=Validacion)
rownames(ResultadosRMSE)<-nombresCálculo de la variación
ResultadosRMSE$Por_variacion<-((ResultadosRMSE$Validacion-ResultadosRMSE$Entrenamiento)/ResultadosRMSE$Entrenamiento)*100
ResultadosRMSE## Entrenamiento Validacion Por_variacion
## lm 9.237 10.412 12.72058
## knn 11.152 12.573 12.74211
## glm 9.082 21.546 137.23849
## árbol 10.021 11.141 11.17653
## bosque 6.963 11.194 60.76404Modelos elegidos
Teniendo como criterio el mínimo RMSE y que la variación entre los datos de entrenamiento y validación no superen el 15 %, se eligieron los siguientes modelos:
Modelo de regresión lineal para predición de Total Accidentes
Se ajusta el modelo con todos los datos desde el 01-01-2014 al 31-12-2018
library(caret)
trcntrl = trainControl(method="cv", number=10)
caret_lm_fit_final = caret::train(TOTAL_ACCIDENTES∼Ano_Base+DIA+SEMANA+Feriado_Lunes+Feriado_Otro+Madre+Semana_Santa+Viernes_Desp_Quincena_v2+Feria_Flores, data=Total_Dataset_Freq,
method = "lm", trControl = trcntrl,
preProcess=c("center", "scale"),
tuneLength = 10)summary(caret_lm_fit_final)##
## Call:
## lm(formula = .outcome ~ ., data = dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -73.865 -9.721 -0.363 9.032 70.719
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 114.69113 0.35332 324.614 < 2e-16 ***
## Ano_Base 0.08121 0.35916 0.226 0.8211
## DIA2 -0.17524 0.49485 -0.354 0.7233
## DIA3 -1.00888 0.49500 -2.038 0.0417 *
## DIA4 -1.22787 0.49680 -2.472 0.0135 *
## DIA5 0.95405 0.53120 1.796 0.0727 .
## DIA6 -3.50731 0.49564 -7.076 2.13e-12 ***
## DIA7 -17.29256 0.49822 -34.708 < 2e-16 ***
## SEMANA02 1.05373 0.49996 2.108 0.0352 *
## SEMANA03 3.50386 0.49925 7.018 3.20e-12 ***
## SEMANA04 3.33331 0.49931 6.676 3.28e-11 ***
## SEMANA05 3.82910 0.49933 7.668 2.86e-14 ***
## SEMANA06 4.50106 0.49931 9.015 < 2e-16 ***
## SEMANA07 5.18987 0.49959 10.388 < 2e-16 ***
## SEMANA08 4.57160 0.49931 9.156 < 2e-16 ***
## SEMANA09 4.37872 0.49959 8.765 < 2e-16 ***
## SEMANA10 5.15547 0.49931 10.325 < 2e-16 ***
## SEMANA11 5.20133 0.50026 10.397 < 2e-16 ***
## SEMANA12 4.65194 0.50783 9.160 < 2e-16 ***
## SEMANA13 4.67356 0.50766 9.206 < 2e-16 ***
## SEMANA14 5.21722 0.50753 10.280 < 2e-16 ***
## SEMANA15 4.96149 0.50756 9.775 < 2e-16 ***
## SEMANA16 3.97923 0.50408 7.894 5.10e-15 ***
## SEMANA17 5.21980 0.49893 10.462 < 2e-16 ***
## SEMANA18 4.93102 0.49965 9.869 < 2e-16 ***
## SEMANA19 4.78638 0.51173 9.353 < 2e-16 ***
## SEMANA20 4.88423 0.49950 9.778 < 2e-16 ***
## SEMANA21 4.95955 0.49923 9.934 < 2e-16 ***
## SEMANA22 4.24158 0.49960 8.490 < 2e-16 ***
## SEMANA23 4.96348 0.49959 9.935 < 2e-16 ***
## SEMANA24 4.66871 0.49960 9.345 < 2e-16 ***
## SEMANA25 3.92896 0.49932 7.869 6.21e-15 ***
## SEMANA26 2.85730 0.49934 5.722 1.23e-08 ***
## SEMANA27 3.96033 0.50064 7.910 4.49e-15 ***
## SEMANA28 4.30223 0.49924 8.618 < 2e-16 ***
## SEMANA29 5.04255 0.49929 10.099 < 2e-16 ***
## SEMANA30 4.83777 0.50544 9.571 < 2e-16 ***
## SEMANA31 5.44505 0.58108 9.371 < 2e-16 ***
## SEMANA32 5.10445 0.56751 8.994 < 2e-16 ***
## SEMANA33 5.21339 0.49950 10.437 < 2e-16 ***
## SEMANA34 4.53636 0.50003 9.072 < 2e-16 ***
## SEMANA35 4.88814 0.49959 9.784 < 2e-16 ***
## SEMANA36 4.74401 0.49931 9.501 < 2e-16 ***
## SEMANA37 5.32209 0.49933 10.658 < 2e-16 ***
## SEMANA38 5.37099 0.49931 10.757 < 2e-16 ***
## SEMANA39 4.56086 0.49924 9.136 < 2e-16 ***
## SEMANA40 5.35633 0.49924 10.729 < 2e-16 ***
## SEMANA41 3.62721 0.49931 7.265 5.60e-13 ***
## SEMANA42 4.55408 0.50066 9.096 < 2e-16 ***
## SEMANA43 4.52849 0.49931 9.070 < 2e-16 ***
## SEMANA44 4.85287 0.49959 9.714 < 2e-16 ***
## SEMANA45 3.88980 0.50064 7.770 1.33e-14 ***
## SEMANA46 4.79411 0.49986 9.591 < 2e-16 ***
## SEMANA47 4.55202 0.49932 9.116 < 2e-16 ***
## SEMANA48 4.01821 0.49959 8.043 1.59e-15 ***
## SEMANA49 4.94092 0.49891 9.904 < 2e-16 ***
## SEMANA50 4.96321 0.49888 9.949 < 2e-16 ***
## SEMANA51 5.46667 0.49893 10.957 < 2e-16 ***
## SEMANA52 2.63841 0.49916 5.286 1.41e-07 ***
## SEMANA53 0.09892 0.38818 0.255 0.7989
## Feriado_Lunes -9.23249 0.40698 -22.685 < 2e-16 ***
## Feriado_Otro -6.00574 0.37856 -15.865 < 2e-16 ***
## Madre 0.94338 0.37489 2.516 0.0119 *
## Semana_Santa -2.78568 0.41266 -6.750 1.99e-11 ***
## Viernes_Desp_Quincena_v2 0.31527 0.41259 0.764 0.4449
## Feria_Flores 1.30801 0.53772 2.433 0.0151 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 15.1 on 1760 degrees of freedom
## Multiple R-squared: 0.6766, Adjusted R-squared: 0.6646
## F-statistic: 56.64 on 65 and 1760 DF, p-value: < 2.2e-16Se guardan el modelo en un objeto de r
saveRDS(caret_lm_fit_final,"../Modelos/Prediccion_Total_Diario.rds")Modelo_Total_diario<-readRDS(file="../Modelos/Prediccion_Total_Diario.rds")Modelo de árbol de regresión para predición de Accidentes Graves
Se ajusta el modelo con todos los datos desde el 01-01-2014 al 31-12-2018
trcntrl = trainControl(method="cv", number=10)
caret_tree_fit_m_final = caret::train(ACCIDENTES_GRAVES∼Ano_Base+DIA+SEMANA+Feriado_Lunes+Feriado_Otro+Madre+Semana_Santa+Viernes_Desp_Quincena_v2+Feria_Flores,data=Total_Dataset_Freq,
method = "rpart", trControl = trcntrl,
parms = list(split = "gini"),
preProcess=c("center", "scale"),
tuneLength = 10)## Warning in nominalTrainWorkflow(x = x, y = y, wts = weights, info =
## trainInfo, : There were missing values in resampled performance measures.caret_tree_fit_m_final## CART
##
## 1826 samples
## 9 predictor
##
## Pre-processing: centered (65), scaled (65)
## Resampling: Cross-Validated (10 fold)
## Summary of sample sizes: 1643, 1645, 1643, 1642, 1643, 1643, ...
## Resampling results across tuning parameters:
##
## cp RMSE Rsquared MAE
## 0.004063053 10.53984 0.3444020 8.256158
## 0.004658364 10.57656 0.3397463 8.285625
## 0.004969618 10.59364 0.3370691 8.296671
## 0.005631103 10.57242 0.3393593 8.266976
## 0.008619910 10.67194 0.3275812 8.351267
## 0.009217134 10.69428 0.3252437 8.355144
## 0.015452952 10.79810 0.3121552 8.442178
## 0.049148474 11.09790 0.2749346 8.590807
## 0.067434570 11.50460 0.2194453 8.899255
## 0.194454760 12.58149 0.1586137 9.804889
##
## RMSE was used to select the optimal model using the smallest value.
## The final value used for the model was cp = 0.004063053.Se guardan el modelo en un objeto de r
saveRDS(caret_tree_fit_m_final,"../Modelos/Prediccion_Grave_Diario.rds")Modelo_Grave_diario<-readRDS(file="../Modelos/Prediccion_Grave_Diario.rds")Modelo de regresión lineal para predición de Accidentes Leves
Se ajusta el modelo con todos los datos desde el 01-01-2014 al 31-12-2018
trcntrl = trainControl(method="cv", number=10)
caret_lm_fit_sd_final = caret::train(ACCIDENTES_LEVES∼Ano_Base+DIA+SEMANA+Feriado_Lunes+Feriado_Otro+Madre+Semana_Santa+Viernes_Desp_Quincena_v2+Feria_Flores, data=Total_Dataset_Freq,
method = "lm", trControl = trcntrl,
preProcess=c("center", "scale"),
tuneLength = 10)summary(caret_lm_fit_sd_final)##
## Call:
## lm(formula = .outcome ~ ., data = dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -58.228 -5.797 -0.440 5.919 40.506
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 51.4639 0.2248 228.923 < 2e-16 ***
## Ano_Base 0.8777 0.2285 3.841 0.000127 ***
## DIA2 0.3710 0.3149 1.178 0.238789
## DIA3 -0.5645 0.3150 -1.792 0.073275 .
## DIA4 -0.9083 0.3161 -2.874 0.004107 **
## DIA5 1.3032 0.3380 3.856 0.000120 ***
## DIA6 -2.0862 0.3154 -6.615 4.91e-11 ***
## DIA7 -10.8172 0.3170 -34.122 < 2e-16 ***
## SEMANA02 0.9910 0.3181 3.115 0.001868 **
## SEMANA03 1.9222 0.3177 6.051 1.76e-09 ***
## SEMANA04 2.0861 0.3177 6.566 6.77e-11 ***
## SEMANA05 2.2631 0.3177 7.123 1.53e-12 ***
## SEMANA06 2.3526 0.3177 7.405 2.02e-13 ***
## SEMANA07 3.1001 0.3179 9.752 < 2e-16 ***
## SEMANA08 2.5250 0.3177 7.948 3.36e-15 ***
## SEMANA09 2.5084 0.3179 7.891 5.22e-15 ***
## SEMANA10 2.6935 0.3177 8.478 < 2e-16 ***
## SEMANA11 2.6438 0.3183 8.306 < 2e-16 ***
## SEMANA12 2.6468 0.3231 8.191 4.91e-16 ***
## SEMANA13 2.4947 0.3230 7.723 1.89e-14 ***
## SEMANA14 2.9667 0.3229 9.187 < 2e-16 ***
## SEMANA15 2.9330 0.3230 9.082 < 2e-16 ***
## SEMANA16 2.2395 0.3207 6.982 4.10e-12 ***
## SEMANA17 3.0787 0.3175 9.698 < 2e-16 ***
## SEMANA18 2.8915 0.3179 9.095 < 2e-16 ***
## SEMANA19 2.8266 0.3256 8.681 < 2e-16 ***
## SEMANA20 3.0021 0.3178 9.446 < 2e-16 ***
## SEMANA21 2.7718 0.3176 8.726 < 2e-16 ***
## SEMANA22 2.3476 0.3179 7.385 2.34e-13 ***
## SEMANA23 2.6620 0.3179 8.374 < 2e-16 ***
## SEMANA24 2.5749 0.3179 8.100 1.02e-15 ***
## SEMANA25 2.2623 0.3177 7.121 1.56e-12 ***
## SEMANA26 1.8594 0.3177 5.852 5.77e-09 ***
## SEMANA27 2.2308 0.3185 7.003 3.55e-12 ***
## SEMANA28 2.4724 0.3177 7.783 1.20e-14 ***
## SEMANA29 2.9064 0.3177 9.149 < 2e-16 ***
## SEMANA30 2.6409 0.3216 8.212 4.17e-16 ***
## SEMANA31 2.9031 0.3697 7.852 7.07e-15 ***
## SEMANA32 2.9185 0.3611 8.082 1.17e-15 ***
## SEMANA33 2.9080 0.3178 9.150 < 2e-16 ***
## SEMANA34 2.5326 0.3182 7.960 3.05e-15 ***
## SEMANA35 2.4183 0.3179 7.607 4.53e-14 ***
## SEMANA36 2.6661 0.3177 8.392 < 2e-16 ***
## SEMANA37 2.9097 0.3177 9.158 < 2e-16 ***
## SEMANA38 3.0697 0.3177 9.662 < 2e-16 ***
## SEMANA39 2.5430 0.3177 8.005 2.14e-15 ***
## SEMANA40 3.1543 0.3177 9.930 < 2e-16 ***
## SEMANA41 2.4153 0.3177 7.602 4.70e-14 ***
## SEMANA42 2.7135 0.3186 8.518 < 2e-16 ***
## SEMANA43 2.7366 0.3177 8.614 < 2e-16 ***
## SEMANA44 2.9434 0.3179 9.259 < 2e-16 ***
## SEMANA45 2.5678 0.3185 8.061 1.38e-15 ***
## SEMANA46 3.3625 0.3180 10.572 < 2e-16 ***
## SEMANA47 2.9089 0.3177 9.156 < 2e-16 ***
## SEMANA48 2.6181 0.3179 8.236 3.43e-16 ***
## SEMANA49 3.4415 0.3175 10.841 < 2e-16 ***
## SEMANA50 3.3889 0.3174 10.676 < 2e-16 ***
## SEMANA51 3.5215 0.3175 11.093 < 2e-16 ***
## SEMANA52 1.6008 0.3176 5.040 5.13e-07 ***
## SEMANA53 0.3206 0.2470 1.298 0.194513
## Feriado_Lunes -5.8103 0.2590 -22.437 < 2e-16 ***
## Feriado_Otro -3.8256 0.2409 -15.883 < 2e-16 ***
## Madre 0.6015 0.2385 2.522 0.011773 *
## Semana_Santa -1.2844 0.2626 -4.892 1.09e-06 ***
## Viernes_Desp_Quincena_v2 0.0748 0.2625 0.285 0.775722
## Feria_Flores 0.9906 0.3421 2.895 0.003835 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.606 on 1760 degrees of freedom
## Multiple R-squared: 0.6705, Adjusted R-squared: 0.6583
## F-statistic: 55.1 on 65 and 1760 DF, p-value: < 2.2e-16Se guardan el modelo en un objeto de r
saveRDS(caret_lm_fit_sd_final,"../Modelos/Prediccion_leves_Diario.rds")Modelo_leves_diario<-readRDS(file="../Modelos/Prediccion_leves_Diario.rds")Datos para pronóstico
Se oganizan los datos necesarios para el pronóstico de los accidentes en los años 2019, 2020 y 2021
Importación de los datos
load("../data/Dias_Especiales_Diario.Rda")Dias_Especiales$DIA <-as.factor(format(Dias_Especiales$Fecha,'%u'))
head(Dias_Especiales)## Fecha Ano_Base Lunes martes miercoles jueves viernes sabado domingo
## 1 2014-01-01 0 0 0 1 0 0 0 0
## 2 2014-01-02 0 0 0 0 1 0 0 0
## 3 2014-01-03 0 0 0 0 0 1 0 0
## 4 2014-01-04 0 0 0 0 0 0 1 0
## 5 2014-01-05 0 0 0 0 0 0 0 1
## 6 2014-01-06 0 1 0 0 0 0 0 0
## Enero Febrero Marzo Abril Mayo Junio Julio Agosto Septiembre Octubre
## 1 1 0 0 0 0 0 0 0 0 0
## 2 1 0 0 0 0 0 0 0 0 0
## 3 1 0 0 0 0 0 0 0 0 0
## 4 1 0 0 0 0 0 0 0 0 0
## 5 1 0 0 0 0 0 0 0 0 0
## 6 1 0 0 0 0 0 0 0 0 0
## Noviembre Diciembre Feriado Feriado_v1 Feriado_Lunes Feriado_Otro
## 1 0 0 1 1 0 1
## 2 0 0 0 0 0 0
## 3 0 0 0 0 0 0
## 4 0 0 0 0 0 0
## 5 0 0 0 0 0 0
## 6 0 0 1 1 1 0
## Previo_feriado Semana_Santa Semana_Santa_Mes Semana_Santa_Semana Prima
## 1 0 0 0 0 0
## 2 0 0 0 0 0
## 3 1 0 0 0 0
## 4 1 0 0 0 0
## 5 1 0 0 0 0
## 6 0 0 0 0 0
## Mujer Padre Madre AmoryAmistad Semana_Santa_v1 Viernes_Antes_Puente
## 1 0 0 0 0 0 0
## 2 0 0 0 0 0 0
## 3 0 0 0 0 0 1
## 4 0 0 0 0 0 0
## 5 0 0 0 0 0 0
## 6 0 0 0 0 0 0
## Quincena Viernes_Desp_Quincena Viernes_Desp_Quincena_v1
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Viernes_Desp_Quincena_v2 Feria_Flores Feria_Flores_Mes
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Feria_Flores_Semana ANO SEMANA MES DIA
## 1 0 2014 01 01 3
## 2 0 2014 01 01 4
## 3 0 2014 01 01 5
## 4 0 2014 01 01 6
## 5 0 2014 01 01 7
## 6 0 2014 02 01 1datos_pronostico_diario<-Dias_Especiales[,c("Fecha","Ano_Base","DIA","SEMANA","Feriado_Lunes","Feriado_Otro","Madre","Semana_Santa","Viernes_Desp_Quincena_v2","Feria_Flores")]Predicción del Total de accidentes con el modelo de regresión lineal
datos_pronostico_diario$prediccion_Total<-predict(Modelo_Total_diario,datos_pronostico_diario[,c("Ano_Base","DIA","SEMANA","Feriado_Lunes","Feriado_Otro","Madre","Semana_Santa","Viernes_Desp_Quincena_v2","Feria_Flores")])Predicción de accidentes graves con el modelo de árbol de regresión
datos_pronostico_diario$prediccion_Graves<-predict(Modelo_Grave_diario,datos_pronostico_diario[,c("Ano_Base","DIA","SEMANA","Feriado_Lunes","Feriado_Otro","Madre","Semana_Santa","Viernes_Desp_Quincena_v2","Feria_Flores")])Predicción de accidentes leves con el modelo de regresión lineal
datos_pronostico_diario$prediccion_Leves<-predict(Modelo_leves_diario,datos_pronostico_diario[,c("Ano_Base","DIA","SEMANA","Feriado_Lunes","Feriado_Otro","Madre","Semana_Santa","Viernes_Desp_Quincena_v2","Feria_Flores")])Se guardan los datos de pronóstico en un objeto de r
save(datos_pronostico_diario,file="../Modelos/datos_pronostico_diario.Rda")